essay on technology for farming

Agriculture’s connected future: How technology can yield new growth

The agriculture industry has radically transformed over the past 50 years. Advances in machinery have expanded the scale, speed, and productivity of farm equipment, leading to more efficient cultivation of more land. Seed, irrigation, and fertilizers also have vastly improved, helping farmers increase yields. Now, agriculture is in the early days of yet another revolution, at the heart of which lie data and connectivity. Artificial intelligence, analytics, connected sensors, and other emerging technologies could further increase yields, improve the efficiency of water and other inputs, and build sustainability and resilience across crop cultivation and animal husbandry.

The future of connectivity

As the world experiences a quantum leap in the speed and scope of digital connections, industries are gaining new and enhanced tools to boost productivity and spur innovation. Over the next decade, existing technologies like fiber, low-power wide-area networks (LPWAN), Wi-Fi 6, low- to mid-band 5G, and short-range connections like radio-frequency identification (RFID) will expand their reach as networks are built out and adoption grows. At the same time, new generations of these technologies will appear, with upgraded standards. In addition, new types of more revolutionary—and more capital-intensive—frontier connectivity, like high-band 5G and low-Earth-orbit (LEO) satellites, will begin to come online.

Together, these technological developments will unlock powerful new capabilities across industries. Near-global coverage will allow the expansion of use cases even to remote areas and will enable constant connectivity universally. Massive use of Internet of Things (IoT) applications and use cases will be enabled as new technologies allow very high device densities. And mission-critical services will take advantage of ultralow-latency, high-reliability, and high-security connections.

Without a solid connectivity infrastructure, however, none of this is possible. If connectivity is implemented successfully in agriculture, the industry could tack on $500 billion in additional value to the global gross domestic product by 2030, according to our research. This would amount to a 7 to 9 percent improvement from its expected total and would alleviate much of the present pressure on farmers. It is one of just seven sectors that, fueled by advanced connectivity, will contribute $2 trillion to $3 trillion in additional value to global GDP over the next decade, according to research by the McKinsey Center for Advanced Connectivity  and the McKinsey Global Institute  (MGI) (see sidebar “The future of connectivity”).

Demand for food is growing at the same time the supply side faces constraints in land and farming inputs. The world’s population is on track to reach 9.7 billion by 2050, 1 The World Population Prospects: 2015 Revision, United Nations, Department of Economic and Social Affairs, Population Division, 2015. requiring a corresponding 70 percent increase in calories available for consumption, even as the cost of the inputs needed to generate those calories is rising. 2 World Resources Report: Creating a Sustainable Food Future, United Nations, World Resources Institute, and the World Bank, 2013. By 2030, the water supply will fall 40 percent short of meeting global water needs, 3 World Could Face Water Availability Shortfall by 2030 if Current Trends Continue, Secretary-General Warns at Meeting of High-Level Panel, United Nations, 2016. and rising energy, labor, and nutrient costs are already pressuring profit margins. About one-quarter of arable land is degraded and needs significant restoration before it can again sustain crops at scale. 4 The State of the World’s Land and Water Resources for Food and Agriculture: Managing systems at risk, Food and Agriculture Organization of the United Nations and Earthscan, 2011. And then there are increasing environmental pressures, such as climate change and the economic impact of catastrophic weather events, and social pressures, including the push for more ethical and sustainable farm practices, such as higher standards for farm-animal welfare and reduced use of chemicals and water.

To address these forces poised to further roil the industry, agriculture must embrace a digital transformation enabled by connectivity. Yet agriculture remains less digitized compared with many other industries globally. Past advances were mostly mechanical, in the form of more powerful and efficient machinery, and genetic, in the form of more productive seed and fertilizers. Now much more sophisticated, digital tools are needed to deliver the next productivity leap. Some already exist to help farmers more efficiently and sustainably use resources, while more advanced ones are in development. These new technologies can upgrade decision making, allowing better risk and variability management to optimize yields and improve economics. Deployed in animal husbandry, they can enhance the well-being of livestock, addressing the growing concerns over animal welfare.

Demand for food is growing at the same time the supply side faces constraints in land and farming inputs.

But the industry confronts two significant obstacles. Some regions lack the necessary connectivity infrastructure, making development of it paramount. In regions that already have a connectivity infrastructure, farms have been slow to deploy digital tools because their impact has not been sufficiently proven.

The COVID-19 crisis has further intensified other challenges agriculture faces in five areas: efficiency, resilience, digitization, agility, and sustainability. Lower sales volumes have pressured margins, exacerbating the need for farmers to contain costs further. Gridlocked global supply chains have highlighted the importance of having more local providers, which could increase the resilience of smaller farms. In this global pandemic, heavy reliance on manual labor has further affected farms whose workforces face mobility restrictions. Additionally, significant environmental benefits from decreased travel and consumption during the crisis are likely to drive a desire for more local, sustainable sourcing, requiring producers to adjust long-standing practices. In short, the crisis has accentuated the necessity of more widespread digitization and automation, while suddenly shifting demand and sales channels have underscored the value of agile adaptation.

Current connectivity in agriculture

In recent years, many farmers have begun to consult data about essential variables like soil, crops, livestock, and weather. Yet few if any have had access to advanced digital tools that would help to turn these data into valuable, actionable insights. In less-developed regions, almost all farmwork is manual, involving little or no advanced connectivity or equipment.

Even in the United States, a pioneer country in connectivity, only about one-quarter of farms currently use any connected equipment or devices to access data, and that technology isn’t exactly state-of-the-art, running on 2G or 3G networks that telcos plan to dismantle or on very low-band IoT networks that are complicated and expensive to set up. In either case, those networks can support only a limited number of devices and lack the performance for real-time data transfer, which is essential to unlock the value of more advanced and complex use cases.

Nonetheless, current IoT technologies running on 3G and 4G cellular networks are in many cases sufficient to enable simpler use cases, such as advanced monitoring of crops and livestock. In the past, however, the cost of hardware was high, so the business case for implementing IoT in farming did not hold up. Today, device and hardware costs are dropping rapidly, and several providers now offer solutions at a price we believe will deliver a return in the first year of investment.

These simpler tools are not enough, though, to unlock all the potential value that connectivity holds for agriculture. To attain that, the industry must make full use of digital applications and analytics, which will require low latency, high bandwidth, high resiliency, and support for a density of devices offered by advanced and frontier connectivity technologies like LPWAN, 5G, and LEO satellites (Exhibit 1).

The challenge the industry is facing is thus twofold: infrastructure must be developed to enable the use of connectivity in farming, and where connectivity already exists, strong business cases must be made in order for solutions to be adopted. The good news is that connectivity coverage is increasing almost everywhere. By 2030, we expect advanced connectivity infrastructure of some type to cover roughly 80 percent of the world’s rural areas; the notable exception is Africa, where only a quarter of its area will be covered. The key, then, is to develop more—and more effective—digital tools for the industry and to foster widespread adoption of them.

As connectivity increasingly takes hold, these tools will enable new capabilities in agriculture:

• Massive Internet of Things. Low-power networks and cheaper sensors will set the stage for the IoT to scale up, enabling such use cases as precision irrigation of field crops, monitoring of large herds of livestock, and tracking of the use and performance of remote buildings and large fleets of machinery.
• Mission-critical services. Ultralow latency and improved stability of connections will foster confidence to run applications that demand absolute reliability and responsiveness, such as operating autonomous machinery and drones.
• Near-global coverage. If LEO satellites attain their potential, they will enable even the most remote rural areas of the world to use extensive digitization, which will enhance global farming productivity.

Connectivity’s potential for value creation

By the end of the decade, enhanced connectivity in agriculture could add more than $500 billion to global gross domestic product, a critical productivity improvement of 7 to 9 percent for the industry. 5 This represents our estimate of the total potential for value added in agricultural production; it is not an estimate of the agritech and precision-agriculture market size. Much of that value, however, will require investments in connectivity that today are largely absent from agriculture. Other industries already use technologies like LPWAN, cloud computing, and cheaper, better sensors requiring minimal hardware, which can significantly reduce the necessary investment. We have analyzed five use cases—crop monitoring, livestock monitoring, building and equipment management, drone farming, and autonomous farming machinery—where enhanced connectivity is already in the early stages of being used and is most likely to deliver the higher yields, lower costs, and greater resilience and sustainability that the industry needs to thrive in the 21st century (Exhibit 2).

It’s important to note that use cases do not apply equally across regions. For example, in North America, where yields are already fairly optimized, monitoring solutions do not have the same potential for value creation as in Asia or Africa, where there is much more room to improve productivity. Drones and autonomous machinery will deliver more impact to advanced markets, as technology will likely be more readily available there (Exhibit 3).

About the use-case research

The value of our agriculture-connectivity use cases resides primarily in labor efficiencies, input optimization, yield increases, reduced overhead, and improvements in operation and maintenance of machinery. Each use case enables a series of improvement levers in those areas that promise to enhance the productivity of farming (exhibit).

We applied those levers to the profitability drivers of agricultural production to derive an economic potential for the industry as a whole. For example, a use case might enable a 5 to 10 percent reduction in fertilizer usage, saving costs for the farmer, or enable 3 percent higher yields, leading to greater revenues for the farmer. In fact, higher yields represent the largest opportunity, with advanced connectivity potentially adding some $350 billion of value to global food production without additional inputs or labor costs.

Potential value initially will accrue to large farms that have more investing power and better incentives to digitize. Connectivity promises easier surveying of large tracts, and the fixed costs of developing IoT solutions are more easily offset in large production facilities than on small family farms. Crops like cereals, grains, fruits, and vegetables will generate most of the value we identified, for similar reasons. Connectivity enables more use cases in these sectors than in meat and dairy, because of the large average size of farms, relatively higher player consolidation, and better applicability of connected technologies, as IoT networks are especially adapted to static monitoring of many variables. It’s also interesting to note that Asia should garner about 60 percent of the total value simply because it produces the biggest volume of crops (see sidebar “About the use-case research”).

Use case 1: Crop monitoring

Connectivity offers a variety of ways to improve the observation and care of crops. Integrating weather data, irrigation, nutrient, and other systems could improve resource use and boost yields by more accurately identifying and predicting deficiencies. For instance, sensors deployed to monitor soil conditions could communicate via LPWAN, directing sprinklers to adjust water and nutrient application. Sensors could also deliver imagery from remote corners of fields to assist farmers in making more informed and timely decisions and getting early warnings of problems like disease or pests.

Smart monitoring could also help farmers optimize the harvesting window. Monitoring crops for quality characteristics—say, sugar content and fruit color—could help farmers maximize the revenue from their crops.

Most IoT networks today cannot support imagery transfer between devices, let alone autonomous imagery analysis, nor can they support high enough device numbers and density to monitor large fields accurately. Narrowband Internet of Things (NB-IoT) and 5G promise to solve these bandwidth and connection-density issues. The use of more and smoother connections between soil, farm equipment, and farm managers could unlock $130 billion to $175 billion in value by 2030.

Use case 2: Livestock monitoring

Preventing disease outbreaks and spotting animals in distress are critical in large-scale livestock management, where most animals are raised in close quarters on a regimen that ensures they move easily through a highly automated processing system. Chips and body sensors that measure temperature, pulse, and blood pressure, among other indicators, could detect illnesses early, preventing herd infection and improving food quality. Farmers are already using ear-tag technology from providers such as Smartbow (part of Zoetis) to monitor cows’ heat, health, and location, or technology from companies such as Allflex to implement comprehensive electronic tracing in case of disease outbreaks.

Similarly, environmental sensors could trigger automatic adjustments in ventilation or heating in barns, lessening distress and improving living conditions that increasingly concern consumers. Better monitoring of animal health and growth conditions could produce $70 billion to $90 billion in value by 2030.

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Use case 3: building and equipment management.

Chips and sensors to monitor and measure levels of silos and warehouses could trigger automated reordering, reducing inventory costs for farmers, many of whom are already using such systems from companies like Blue Level Technologies. Similar tools could also improve shelf life of inputs and reduce post-harvest losses by monitoring and automatically optimizing storage conditions. Monitoring conditions and usage of buildings and equipment also has the potential to reduce energy consumption. Computer vision and sensors attached to equipment and connected to predictive-maintenance systems could decrease repair costs and extend machinery and equipment life.

Such solutions could achieve $40 billion to $60 billion in cost savings by 2030.

Use case 4: Farming by drone

Agriculture has been using drones for some two decades, with farmers around the world relying on pioneers like Yamaha’s RMAX remote-controlled helicopter to help with crop spraying. Now the next generation of drones is starting to impact the sector, with the ability to survey crops and herds over vast areas quickly and efficiently or as a relay system for ferrying real-time data to other connected equipment and installations. Drones also could use computer vision to analyze field conditions and deliver precise interventions like fertilizers, nutrients, and pesticides where crops most need them. Or they could plant seed in remote locations, lowering equipment and workforce costs. By reducing costs and improving yields, the use of drones could generate between $85 billion and $115 billion in value.

Use case 5: Autonomous farming machinery

More precise GPS controls paired with computer vision and sensors could advance the deployment of smart and autonomous farm machinery. Farmers could operate a variety of equipment on their field simultaneously and without human intervention, freeing up time and other resources. Autonomous machines are also more efficient and precise at working a field than human-operated ones, which could generate fuel savings and higher yields. Increasing the autonomy of machinery through better connectivity could create $50 billion to $60 billion of additional value by 2030.

Additional sources of value

Connected technologies offer an additional, indirect benefit, the value of which is not included in the estimates given in these use cases. The global farming industry is highly fragmented, with most labor done by individual farm owners. Particularly in Asia and Africa, few farms employ outside workers. On such farms, the adoption of connectivity solutions should free significant time for farmers, which they can use to farm additional land for pay or to pursue work outside the industry.

We find the value of deploying advanced connectivity on these farms to achieve such labor efficiencies represents almost $120 billion, bringing the total value of enhanced connectivity from direct and indirect outcomes to more than $620 billion by 2030. The extent to which this value will be captured, however, relies largely on advanced connectivity coverage, which is expected to be fairly low, around 25 percent, in Africa and poorer parts of Asia and Latin America. Achieving the critical mass of adopters needed to make a business case for deploying advanced connectivity also will be more difficult in those regions, where farming is more fragmented than in North America and Europe.

Connected world: An evolution in connectivity beyond the 5G revolution

Implications for the agricultural ecosystem.

As the agriculture industry digitizes, new pockets of value will likely be unlocked. To date, input providers selling seed, nutrients, pesticides, and equipment have played a critical role in the data ecosystem because of their close ties with farmers, their own knowledge of agronomy, and their track record of innovation. For example, one of the world’s largest fertilizer distributors now offers both fertilizing agents and software that analyzes field data to help farmers determine where to apply their fertilizers and in what quantity. Similarly, a large-equipment manufacturer is developing precision controls that make use of satellite imagery and vehicle-to-vehicle connections to improve the efficiency of field equipment.

Advanced connectivity does, however, give new players an opportunity to enter the space. For one thing, telcos and LPWAN providers have an essential role to play in installing the connectivity infrastructure needed to enable digital applications on farms. They could partner with public authorities and other agriculture players to develop public or private rural networks, capturing some of the new value in the process.

Agritech companies are another example of the new players coming into the agriculture sphere. They specialize in offering farmers innovative products that make use of technology and data to improve decision making and thereby increase yields and profits. Such agritech enterprises could proffer solutions and pricing models that reduce perceived risk for farmers—with, for example, subscription models that remove the initial investment burden and allow farmers to opt out at any time—likely leading to faster adoption of their products. An Italian agritech is doing this by offering to monitor irrigation and crop protection for wineries at a seasonal, per-acre fee inclusive of hardware installation, data collection and analysis, and decision support. Agritech also could partner with agribusinesses to develop solutions.

Still, much of this cannot happen until many rural areas get access to a high-speed broadband network. We envision three principal ways the necessary investment could take place to make this a reality:

• Telco-driven deployment. Though the economics of high-bandwidth rural networks have generally been poor, telcos could benefit from a sharp increase in rural demand for their bandwidth as farmers embrace advanced applications and integrated solutions.
• Provider-driven deployment. Input providers, with their existing industry knowledge and relationships, are probably best positioned to take the lead in connectivity-related investment. They could partner with telcos or LPWAN businesses to develop rural connectivity networks and then offer farmers business models integrating connected technology and product and decision support.
• Farmer-driven deployment. Farm owners, alone or in tandem with LPWAN groups or telcos, could also drive investment. This would require farmers to develop the knowledge and skills to gather and analyze data locally, rather than through third parties, which is no small hurdle. But farmers would retain more control over data.

How to do it

Regardless of which group drives the necessary investment for connectivity in agriculture, no single entity will be able to go it alone. All of these advances will require the industry’s main actors to embrace collaboration as an essential aspect of doing business. Going forward, winners in delivering connectivity to agriculture will need deep capabilities across various domains, ranging from knowledge of farm operations to advanced data analytics and the ability to offer solutions that integrate easily and smoothly with other platforms and adjacent industries. For example, data gathered by autonomous tractors should seamlessly flow to the computer controlling irrigation devices, which in turn should be able to use weather-station data to optimize irrigation plans.

Connectivity pioneers in the industry, however, have already started developing these new capabilities internally. Organizations prefer keeping proprietary data on operations internal for confidentiality and competitive reasons. This level of control also makes the data easier to analyze and helps the organization be more responsive to evolving client needs.

But developing new capabilities is not the end game. Agriculture players able to develop partnerships with telcos or LPWAN players will gain significant leverage in the new connected-agriculture ecosystem. Not only will they be able to procure connectivity hardware more easily and affordably through those partnerships, they will also be better positioned to develop close relationships with farmers as connectivity becomes a strategic issue. Input providers or distributors could thus find themselves in a connectivity race. If input providers manage to develop such partnerships, they could connect directly with farmers and cut out distributors entirely. If distributors win that race, they will consolidate their position in the value chain by remaining an essential intermediary, closer to the needs of farmers.

The public sector also could play a role by improving the economics of developing broadband networks, particularly in rural areas. For example, the German and Korean governments have played a major role in making network development more attractive by heavily subsidizing spectrum or providing tax breaks to telcos. 6 “Das Breitbandförderprogramm des Bundes” [in German], Bundesministerium für Verkehr und digitale Infrastruktur, 2020, bmvi.de; 5G in Korea: Volume 1: Get a taste of the future, Samsung Electronics, 2019, samsungnetworks.com. Other regions could replicate this model, accelerating development of connective products by cost-effectively giving input providers and agritech companies assurance of a backbone over which they could deliver services. Eventual deployment of LEO satellite constellations would likely have a similar impact.

Agriculture, one of the world’s oldest industries, finds itself at a technological crossroads. To handle increasing demand and several disruptive trends successfully, the industry will need to overcome the challenges to deploying advanced connectivity. This will require significant investment in infrastructure and a realignment of traditional roles. It is a huge but critical undertaking, with more than $500 billion in value at stake. The success and sustainability of one of the planet’s oldest industries may well depend on this technology transformation, and those that embrace it at the outset may be best positioned to thrive in agriculture’s connectivity-driven future.

Lutz Goedde is a senior partner and global leader of McKinsey’s Agriculture Practice in the Denver office; Joshua Katz is a partner in the Stamford office; and Alexandre Menard is a senior partner in the Paris office, where Julien Revellat is an associate partner.

The authors wish to thank Nicolas D. Estais, Claus Gerckens, Vincent Tourangeau, and the McKinsey Center for Advanced Connectivity for their contributions to the article.

This article was edited by Daniel Eisenberg, a senior editor in the New York office.

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• Published: 27 April 2017

Technology: The Future of Agriculture

• Anthony King  

Nature volume  544 ,  pages S21–S23 ( 2017 ) Cite this article

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• Agriculture

A technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice.

Over the centuries, as farmers have adopted more technology in their pursuit of greater yields, the belief that 'bigger is better' has come to dominate farming, rendering small-scale operations impractical. But advances in robotics and sensing technologies are threatening to disrupt today's agribusiness model. “There is the potential for intelligent robots to change the economic model of farming so that it becomes feasible to be a small producer again,” says robotics engineer George Kantor at Carnegie Mellon University in Pittsburgh, Pennsylvania.

Twenty-first century robotics and sensing technologies have the potential to solve problems as old as farming itself. “I believe, by moving to a robotic agricultural system, we can make crop production significantly more efficient and more sustainable,” says Simon Blackmore, an engineer at Harper Adams University in Newport, UK. In greenhouses devoted to fruit and vegetable production, engineers are exploring automation as a way to reduce costs and boost quality (see ‘ Ripe for the picking ’). Devices to monitor vegetable growth, as well as robotic pickers, are currently being tested. For livestock farmers, sensing technologies can help to manage the health and welfare of their animals (‘ Animal trackers ’). And work is underway to improve monitoring and maintenance of soil quality (‘ Silicon soil saviours ’), and to eliminate pests and disease without resorting to indiscriminate use of agrichemicals (‘ Eliminating enemies ’).

Although some of these technologies are already available, most are at the research stage in labs and spin-off companies. “Big-machinery manufacturers are not putting their money into manufacturing agricultural robots because it goes against their current business models,” says Blackmore. Researchers such as Blackmore and Kantor are part of a growing body of scientists with plans to revolutionize agricultural practice. If they succeed, they'll change how we produce food forever. “We can use technology to double food production,” says Richard Green, agricultural engineer at Harper Adams.

Ripe for the picking

The Netherlands is famed for the efficiency of its fruit- and vegetable-growing greenhouses, but these operations rely on people to pick the produce. “Humans are still better than robots, but there is a lot of effort going into automatic harvesting,” says Eldert van Henten, an agricultural engineer at Wageningen University in the Netherlands, who is working on a sweet-pepper harvester. The challenge is to quickly and precisely identify the pepper and avoid cutting the main stem of the plant. The key lies in fast, precise software. “We are performing deep learning with the machine so it can interpret all the data from a colour camera fast,” says van Henten. “We even feed data from regular street scenes into the neural network to better train it.”

In the United Kingdom, Green has developed a strawberry harvester that he says can pick the fruit faster than humans. It relies on stereoscopic vision with RGB cameras to capture depth, but it is its powerful algorithms that allow it to pick a strawberry every two seconds. People can pick 15 to 20 a minute, Green estimates. “Our partners at the National Physical Laboratory worked on the problem for two years, but had a brainstorm one day and finally cracked it,” says Green, adding that the solution is too commercially sensitive to share. He thinks that supervised groups of robots can step into the shoes of strawberry pickers in around five years. Harper Adams University is considering setting up a spin-off company to commercialize the technology. The big hurdle to commercialization, however, is that food producers demand robots that can pick all kinds of vegetables, says van Henten. The variety of shapes, sizes and colours of tomatoes, for instance, makes picking them a tough challenge, although there is already a robot available to remove unwanted leaves from the plants.

Another key place to look for efficiencies is timing. Picking too early is wasteful because you miss out on growth, but picking too late slashes weeks off the storage time. Precision-farming engineer Manuela Zude-Sasse at the Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany, is attaching sensors to apples to detect their size, and levels of the pigments chlorophyll and anthocyanin. The data are fed into an algorithm to calculate developmental stage, and, when the time is ripe for picking, growers are alerted by smartphone.

So far, Zude-Sasse has put sensors on pears, citrus fruits, peaches, bananas and apples ( pictured ). She is set to start field trials later this year in a commercial tomato greenhouse and an apple orchard. She is also developing a smartphone app for cherry growers. The app will use photographs of cherries taken by growers to calculate growth rate and a quality score.

Growing fresh fruit and vegetables is all about keeping the quality high while minimizing costs. “If you can schedule harvest to optimum fruit development, then you can reap an economic benefit and a quality one,” says Zude-Sasse.

Eliminating enemies

The Food and Agriculture Organization of the United Nations estimates that 20–40% of global crop yields are lost each year to pests and diseases, despite the application of around two-million tonnes of pesticide. Intelligent devices, such as robots and drones, could allow farmers to slash agrichemical use by spotting crop enemies earlier to allow precise chemical application or pest removal, for example. “The market is demanding foods with less herbicide and pesticide, and with greater quality,” says Red Whittaker, a robotics engineer at Carnegie Mellon who designed and patented an automated guidance system for tractors in 1997. “That challenge can be met by robots.”

“We predict drones, mounted with RGB or multispectral cameras, will take off every morning before the farmer gets up, and identify where within the field there is a pest or a problem,” says Green. As well as visible light, these cameras would be able to collect data from the invisible parts of the electromagnetic spectrum that could allow farmers to pinpoint a fungal disease, for example, before it becomes established. Scientists from Carnegie Mellon have begun to test the theory in sorghum ( Sorghum bicolor ), a staple in many parts of Africa and a potential biofuel crop in the United States.

Agribotix, an agriculture data-analysis company in Boulder, Colorado, supplies drones and software that use near-infrared images to map patches of unhealthy vegetation in large fields. Images can also reveal potential causes, such as pests or problems with irrigation. The company processes drone data from crop fields in more than 50 countries. It is now using machine learning to train its systems to differentiate between crops and weeds, and hopes to have this capability ready for the 2017 growing season. “We will be able to ping growers with an alert saying you have weeds growing in your field, here and here,” says crop scientist Jason Barton, an executive at Agribotix.

Modern technology that can autonomously eliminate pests and target agrichemicals better will reduce collateral damage to wildlife, lower resistance and cut costs. “We are working with a pesticide company keen to apply from the air using a drone,” says Green. Rather than spraying a whole field, the pesticide could be delivered to the right spot in the quantity needed, he says. The potential reductions in pesticide use are impressive. According to researchers at the University of Sydney's Australian Centre for Field Robotics, targeted spraying of vegetables used 0.1% of the volume of herbicide used in conventional blanket spraying. Their prototype robot is called RIPPA (Robot for Intelligent Perception and Precision Application) and shoots weeds with a directed micro-dose of liquid. Scientists at Harper Adams are going even further, testing a robot that does away with chemicals altogether by blasting weeds close to crops with a laser. “Cameras identify the growing point of the weed and our laser, which is no more than a concentrated heat source, heats it up to 95 °C, so the weed either dies or goes dormant,” says Blackmore.

Animal trackers

Smart collars — a bit like the wearable devices designed to track human health and fitness — have been used to monitor cows in Scotland since 2010. Developed by Glasgow start-up Silent Herdsman, the collar monitors fertility by tracking activity — cows move around more when they are fertile — and uses this to alert farmers to when a cow is ready to mate, sending a message to his or her laptop or smartphone. The collars ( pictured ), which are now being developed by Israeli dairy-farm-technology company Afimilk after they acquired Silent Herdsman last year, also detect early signs of illness by monitoring the average time each cow spends eating and ruminating, and warning the farmer via a smartphone if either declines.

“We are now looking at more subtle behavioural changes and how they might be related to animal health, such as lameness or acidosis,” says Richard Dewhurst, an animal nutritionist at Scotland's Rural College (SRUC) in Edinburgh, who is involved in research to expand the capabilities of the collar. Scientists are developing algorithms to interrogate data collected by the collars.

In a separate project, Dewhurst is analysing levels of exhaled ketones and sulfides in cow breath to reveal underfeeding and tissue breakdown or excess protein in their diet. “We have used selected-ionflow-tube mass spectrometry, but there are commercial sensors available,” says Dewhurst.

Cameras are also improving the detection of threats to cow health. The inflammatory condition mastitis — often the result of a bacterial infection — is one of the biggest costs to the dairy industry, causing declines in milk production or even death. Thermal-imaging cameras installed in cow sheds can spot hot, inflamed udders, allowing animals to be treated early.

Carol-Anne Duthie, an animal scientist at SRUC, is using 3D cameras to film cattle at water troughs to estimate the carcass grade (an assessment of the quality of a culled cow) and animal weight. These criteria determine the price producers are paid. Knowing the optimum time to sell would maximize profit and provide abattoirs with more-consistent animals. “This has knock on effects in terms of overall efficiency of the entire supply chain, reducing the animals which are out of specification reaching the abattoir,” Duthie explains.

And researchers in Belgium have developed a camera system to monitor broiler chickens in sheds. Three cameras continually track the movements of thousands of individual birds to spot problems quickly. “Analysing the behaviour of broilers can give an early warning for over 90% of problems,” says bioengineer Daniel Berckmans at the University of Leuven. The behaviour-monitoring system is being sold by Fancom, a livestock-husbandry firm in Panningen, the Netherlands. The Leuven researchers have also launched a cough monitor to flag respiratory problems in pigs, through a spin-off company called SoundTalks. This can give a warning 12 days earlier than farmers or vets would normally be able to detect a problem, says Berckmans. The microphone, which is positioned above animals in their pen, identifies sick individuals so that treatment can be targeted. “The idea was to reduce the use of antibiotics,” says Berckmans.

Berckmans is now working on downsizing a stress monitor designed for people so that it will attach to a cow's ear tag. “The more you stress an animal, the less energy is available from food for growth,” he says. The monitor takes 200 physiological measurements a second, alerting farmers through a smartphone when there is a problem.

Silicon soil saviours

The richest resource for arable farmers is soil. But large harvesters damage and compact soil, and overuse of agrichemicals such as nitrogen fertilizer are bad for both the environment and a farmer's bottom line. Robotics and autonomous machines could help.

Data from drones are being used for smarter application of nitrogen fertilizer. “Healthy vegetation reflects more near-infrared light than unhealthy vegetation,” explains Barton. The ratio of red to near-infrared bands on a multispectral image can be used to estimate chlorophyll concentration and, therefore, to map biomass and see where interventions such as fertilization are needed after weather or pest damage, for example. When French agricultural technology company Airinov, which offers this type of drone survey, partnered with a French farming cooperative, they found that over a period of 3 years, in 627 fields of oilseed rape ( Brassica napus ), farmers used on average 34 kilograms less nitrogen fertilizer per hectare than they would without the survey data. This saved on average €107 (US$115) per hectare per year.

Bonirob ( pictured ) — a car-sized robot originally developed by a team of scientists including those at Osnabrück University of Applied Sciences in Germany — can measure other indicators of soil quality using various sensors and modules, including a moisture sensor and a penetrometer, which is used to assess soil compaction. According to Arno Ruckelshausen, an agricultural technologist at Osnabrück, Bonirob can take a sample of soil, liquidize it and analyse it to precisely map in real time characteristics such as pH and phosphorous levels. The University of Sydney's smaller RIPPA robot can also detect soil characteristics that affect crop production, by measuring soil conductivity.

Soil mapping opens the door to sowing different crop varieties in one field to better match shifting soil properties such as water availability. “You could differentially seed a field, for example, planting deep-rooting barley or wheat varieties in more sandy parts,” says Maurice Moloney, chief executive of the Global Institute for Food Security in Saskatoon, Canada. Growing multiple crops together could also lead to smarter use of agrichemicals. “Nature is strongly against monoculture, which is one reason we have to use massive amounts of herbicide and pesticides,” says van Henten. “It is about making the best use of resources.”

Mixed sowing would challenge an accepted pillar of agricultural wisdom: that economies of scale and the bulkiness of farm machinery mean vast fields of a single crop is the most-efficient way to farm, and the bigger the machine, the more-efficient the process. Some of the heaviest harvesters weigh 60 tonnes, cost more than a top-end sports car and leave a trail of soil compaction in their wake that can last for years.

But if there is no need for the farmer to drive the machine, then one large vehicle that covers as much area as possible is no longer needed. “As soon as you remove the human component, size is irrelevant,” says van Henten. Small, autonomous robots make mixed planting feasible and would not crush the soil.

In April, researchers at Harpers Adams began a proof-of-concept experiment with a hectare of barley. “We plan to grow and harvest the entire crop from start to finish with no humans entering the field,” says Green. The experiment will use existing machinery, such as tractors, that have been made autonomous, rather than new robots, but their goal is to use the software developed during this trial as the brains of purpose-built robots in the future. “Robots can facilitate a new way of doing agriculture,” says van Henten. Many of these disruptive technologies may not be ready for the prime time just yet, but the revolution is coming.

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King, A. Technology: The Future of Agriculture. Nature 544 , S21–S23 (2017). https://doi.org/10.1038/544S21a

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The Future of Farming: Artificial Intelligence and Agriculture

While artificial intelligence (AI) seemed until recently to be science fiction, countless corporations across the globe are now researching ways to implement this technology in everyday life. AI works by processing large quantities of data, interpreting patterns in that data, and then translating these interpretations into actions that resemble those of a human being. Scientists have used it to develop self-driving cars and chess-playing computers, but the technology has expanded into another domain: agriculture. AI has the potential to spur more efficient methods of farming in order to combat global warming, but only with expanded regulation of its development.

Global Warming and Agriculture: A Vicious Cycle

Global warming continues to threaten every aspect of our everyday lives, including crop production. It will reduce the soil moisture in areas close to the equator while leaving northern countries virtually unscathed, according to a study from Wageningen University. We are already seeing the impact of these modified growing conditions on our food production in the form of lower crop yields .

Reduced food production has an especially devastating impact on developing countries. Climate change causes the loss of 35 trillion consumable food calories per year and harms poorer countries who do not have the money to import food. The result is growing food insecurity. And rising sea levels only compound the problem. By the year 2100, sea levels are expected to rise by one meter, which will have a detrimental impact on growers on the coasts whose crops cannot survive in areas where the water is too salty.

However, agriculture is not just a victim of global warming, but also a cause. Agriculture is part of a vicious cycle in which farming leads to global warming, which in turn devastates agricultural production. The process of clearing land for agriculture results in widespread deforestation and contributes to 40 percent of global methane production. Therefore, to confront climate change, it is necessary to ensure reforestation—but how? What is the path to efficient, environmentally-conscious farming?

The Benefits of AI for Environmentally-Conscious Agriculture

This is where AI enters the scene. Farmers use AI for methods such as precision agriculture ; they can monitor crop moisture, soil composition, and temperature in growing areas, enabling farmers to increase their yields by learning how to take care of their crops and determine the ideal amount of water or fertilizer to use.

Furthermore, this technology may have the capacity to reduce deforestation by allowing humans to grow food in urban areas. One Israeli tech company used AI algorithms that create optimal light and water conditions to grow crops in a container small enough to be stored inside a  home. The technology could be especially beneficial for countries in Latin America and the Caribbean, where much of the population lives in cities. Furthermore, the ability to grow food in pre-existing urban areas suggests that humans could become less dependent on deforestation for food production.

Additionally, AI can help locate and therefore protect carbon sinks , forest areas that absorb carbon dioxide from the atmosphere. Otherwise, continued efforts to clear these forests will release more carbon dioxide into the atmosphere. Furthermore, some AI is being developed that can find and target weeds in a field with the appropriate amount of herbicide, eliminating the need for farmers to spread chemicals across entire fields and pollute the surrounding ecosystem. Some countries are already implementing AI into their agricultural  methods. Some farmers in Argentina are already using digital agriculture; there are already AI farms in China .

AI can also be used to curb global warming outside of agriculture. The technology can be used to monitor how efficiently buildings are using energy and monitor urban heat islands. Urban heat islands are first created when urban building materials like concrete and asphalt absorb heat, causing cities to grow hotter than the rest of their surroundings. People then rely more heavily on air conditioning throughout the day in order to stay cool, and the energy used for these services results in greater greenhouse gas emissions. Providing information about the location of these islands could help politicians determine what policies they should adopt to reduce emissions and encourage more efficient and environmentally-conscious city planning.

The Risks of AI

Nonetheless, AI is far from a silver bullet—it could actually contribute to global warming. Due to the large amount of data that AI needs to process, training a single AI releases five times the emissions that an average car would emit during its lifetime, thereby adding to the already substantial environmental impact of computing technology. Data storage and processing centers that deliver digital services like entertainment and cloud computing are already responsible for two percent of global greenhouse gas emissions, a number comparable to the overall percentage of pollution contributed by the aviation industry. Although this statistic may not seem overwhelming, it does suggest that the environmental costs of AI will need to be reduced before expanding the technology on a global scale. Some researchers are already working on developing a standard metric that researchers can use to compare how efficient their particular AI systems might be, ultimately encouraging innovators to create environmentally-friendly data-processing.

Further, securing access to AI on a global scale may pose some challenges. Countries will both need experts in the field who can successfully use the technology and internet connection, neither of which are always readily available. Therefore, in order for developing countries to take advantage of the benefits of AI and improve their food security, there will need to be a focus on developing the infrastructure necessary for internet access and teaching professionals how to use the technology. Additionally, AI can be expensive . Farmers might go into debt and will not be able to maintain the technology on their own as it suffers everyday wear-and-tear. Those unable to secure access to the technology will lose out to larger farms that can implement AI on a wide scale.

But farm owners themselves will not be the only ones faced with new pressures as a result of AI. New technologies will render many agricultural jobs obsolete as machines are able to accomplish the same tasks as humans. For example, China has created a seven-year pilot program that uses robots instead of humans to run farms. This program does not bode well for the future of jobs in agriculture: many of China’s 250 million farmers could lose their jobs due to increased automation.

Some may argue that the rise of automated jobs is not as threatening as it may seem, especially given the US agricultural labor shortage . However, the situation is not necessarily the same in other countries. Many countries in the Global South remain dependent on the agricultural sector because there are few job opportunities in urban areas. But if farmers can produce more food at a faster rate with machines, they will have an incentive to shift away from hiring humans, placing the livelihoods of many families at risk. Even if farmworkers do not lose their jobs, their wages could decline as they appear less efficient compared to their robot competitors. The result is chronic poverty and inequality.

Looking Forward: The Next Steps for AI in Agriculture

Given these concerns, AI cannot be the only response to climate change. These types of adaptive technologies can mitigate the consequences of climate change, but more sweeping measures are necessary to secure global access to food in the face of rising temperatures. If countries are to develop AI for use in agricultural sectors, global leaders must consider the potential costs, the role of legal institutions, and the environmental consequences of data processing before investing in the technology on a broader scale.

Sydney Young

Sydney is the former Director of Interviews and Perspectives at the HIR. She is interested in health, human rights, and social justice issues.

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Impact of Technology on Agriculture

Technological innovations have greatly shaped agriculture throughout time. From the creation of the plow to the global positioning system (GPS) driven precision farming equipment, humans have developed new ways to make farming more efficient and grow more food. We are constantly working to find new ways to irrigate crops or breed more disease resistant varieties. These iterations are key to feeding the ever-expanding global population with the decreasing freshwater supply.

Explore developments in agricultural technology and its impacts on civilization with this curated collection of classroom resources.

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Othmane Friha, Mohamed Amine Ferrag, Lei Shu, Leandros Maglaras, and Xiaochan Wang, "Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies," vol. 8, no. 4, pp. 718-752, Apr. 2021. doi:

Othmane Friha, Mohamed Amine Ferrag, Lei Shu, Leandros Maglaras, and Xiaochan Wang, "Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies," vol. 8, no. 4, pp. 718-752, Apr. 2021. doi:

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

Doi:  10.1109/jas.2021.1003925.

• Othmane Friha 1 ,  , 
• Mohamed Amine Ferrag 2 ,  , 
• Lei Shu 3, 4 ,  ,  , 
• Leandros Maglaras 5 ,  , 
• Xiaochan Wang 6 , 

Networks and Systems Laboratory, University of Badji Mokhtar-Annaba, Annaba 23000, Algeria

Department of Computer Science, Guelma University, Gulema 24000, Algeria

College of Engineering, Nanjing Agricultural University, Nanjing 210095, China

School of Engineering, University of Lincoln, Lincoln LN67TS, UK

School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK

Department of Electrical Engineering, Nanjing Agricultural University, Nanjing 210095, China

Othmane Friha received the master degree in computer science from Badji Mokhtar-Annaba University, Algeria, in 2018. He is currently working toward the Ph.D. degree in the University of Badji Mokhtar-Annaba, Algeria. His current research interests include network and computer security, internet of things (IoT), and applied cryptography

Mohamed Amine Ferrag received the bachelor degree (June, 2008), master degree (June, 2010), Ph.D. degree (June, 2014), HDR degree (April, 2019) from Badji Mokhtar-Annaba University, Algeria, all in computer science. Since October 2014, he is a Senior Lecturer at the Department of Computer Science, Guelma University, Algeria. Since July 2019, he is a Visiting Senior Researcher, NAULincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University. His research interests include wireless network security, network coding security, and applied cryptography. He is featured in Stanford University’s list of the world’s Top 2% Scientists for the year 2019. He has been conducting several research projects with international collaborations on these topics. He has published more than 60 papers in international journals and conferences in the above areas. Some of his research findings are published in top-cited journals, such as the IEEE Communications Surveys and Tutorials , IEEE Internet of Things Journal , IEEE Transactions on Engineering Management , IEEE Access , Journal of Information Security and Applications (Elsevier), Transactions on Emerging Telecommunications Technologies (Wiley), Telecommunication Systems (Springer), International Journal of Communication Systems (Wiley), Sustainable Cities and Society (Elsevier), Security and Communication Networks (Wiley), and Journal of Network and Computer Applications (Elsevier). He has participated in many international conferences worldwide, and has been granted short-term research visitor internships to many renowned universities including, De Montfort University, UK, and Istanbul Technical University, Turkey. He is currently serving on various editorial positions such as Editorial Board Member in Journals (Indexed SCI and Scopus) such as, IET Networks and International Journal of Internet Technology and Secured Transactions (Inderscience Publishers)

Lei Shu (M’07–SM’15) received the B.S. degree in computer science from South Central University for Nationalities in 2002, and the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Ireland, in 2010. Until 2012, he was a Specially Assigned Researcher with the Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Japan. He is currently a Distinguished Professor with Nanjing Agricultural University and a Lincoln Professor with the University of Lincoln, U.K. He is also the Director of the NAU-Lincoln Joint Research Center of Intelligent Engineering. He has published over 400 papers in related conferences, journals, and books in the areas of sensor networks and internet of things (IoT). His current H-index is 54 and i10-index is 197 in Google Scholar Citation. His current research interests include wireless sensor networks and IoT. He has also served as a TPC Member for more than 150 conferences, such as ICDCS, DCOSS, MASS, ICC, GLOBECOM, ICCCN, WCNC, and ISCC. He was a Recipient of the 2014 Top Level Talents in Sailing Plan of Guangdong Province, China, the 2015 Outstanding Young Professor of Guangdong Province, and the GLOBECOM 2010, ICC 2013, ComManTel 2014, WICON 2016, SigTelCom 2017 Best Paper Awards, the 2017 and 2018 IEEE Systems Journal Best Paper Awards, the 2017 Journal of Network and Computer Applications Best Research Paper Award, and the Outstanding Associate Editor Award of 2017, and the 2018 IEEE ACCESS. He has also served over 50 various Co-Chair for international conferences/workshops, such as IWCMC, ICC, ISCC, ICNC, Chinacom, especially the Symposium Co-Chair for IWCMC 2012, ICC 2012, the General Co-Chair for Chinacom 2014, Qshine 2015, Collaboratecom 2017, DependSys 2018, and SCI 2019, the TPC Chair for InisCom 2015, NCCA 2015, WICON 2016, NCCA 2016, Chinacom 2017, InisCom 2017, WMNC 2017, and NCCA 2018

Leandros Maglaras (SM’15) received the B.Sc. degree from Aristotle University of Thessaloniki, Greece, in 1998, M.Sc. in industrial production and management from University of Thessaly in 2004, and M.Sc. and Ph.D. degrees in electrical & computer engineering from University of Volos in 2008 and 2014, respectively. He is the Head of the National Cyber Security Authority of Greece and a Visiting Lecturer in the School of Computer Science and Informatics at the De Montfort University, U.K. He serves on the Editorial Board of several International peer-reviewed journals such as IEEE Access , Wiley Journal on Security & Communication Networks , EAI Transactions on e-Learning and EAI Transactions on Industrial Networks and Intelligent Systems . He is an author of more than 80 papers in scientific magazines and conferences and is a Senior Member of IEEE. His research interests include wireless sensor networks and vehicular ad hoc networks

Xiaochan Wang is currently a Professor in the Department of Electrical Engineering at Nanjing Agricultural University. His main research fields include intelligent equipment for horticulture and intelligent measurement and control. He is an ASABE Member, and the Vice Director of CSAM (Chinese Society for Agricultural Machinery), and also the Senior Member of Chinese Society of Agricultural Engineering. He was awarded the Second Prize of Science and Technology Invention by the Ministry of Education (2016) and the Advanced Worker for Chinese Society of Agricultural Engineering (2012), and he also gotten the “Blue Project” in Jiangsu province young and middle-aged academic leaders (2010)

• Corresponding author: Lei Shu, e-mail: [email protected]
• Revised Date: 2020-11-25
• Accepted Date: 2020-12-30
• Agricultural internet of things (IoT) , 
• internet of things (IoT) , 
• smart agriculture , 
• smart farming , 
• sustainable agriculture

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通讯作者: 陈斌, [email protected]

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• We review the emerging technologies used by the Internet of Things for the future of smart agriculture.
• We provide a classification of IoT applications for smart agriculture into seven categories, including, smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices.
• We provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.
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• Figure 1. The four agricultural revolutions
• Figure 2. Survey structure
• Figure 3. IoT-connected smart agriculture sensors enable the IoT
• Figure 4. The architecture of a typical IoT sensor node
• Figure 5. Fog computing-based agricultural IoT
• Figure 6. SDN/NFV architecture for smart agriculture
• Figure 7. Classification of IoT applications for smart agriculture
• Figure 8. Greenhouse system [ 101 ]
• Figure 9. Aerial-ground robotics system [ 67 ]
• Figure 10. Photovoltaic agri-IoT schematic diagram [ 251 ]
• Figure 11. Smart dairy farming system [ 254 ]
• Figure 12. IoT-based solar insecticidal lamp [ 256 ], [ 257 ]

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Farming Reimagined: A case study of autonomous farm equipment and creating an innovation opportunity space for broadacre smart farming

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• https://doi.org/10.1016/j.njas.2019.100307

1 Introduction

2 background, 3 agricultural equipment and smart farming, 4 smart farming challenges: stakeholder views, 5 canada and the western canadian prairie region, 6 conceptual framework, 7 methodology, 9 discussion, 10 conclusion, declaration of competing interest.

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As agriculture meets digital technologies, a new frontier of innovation is emerging and creating multiple pathways to a smart farming future. This paper presents a case study of a smart farming innovation originating from a small-to-medium sized enterprise (SME) that designs and manufactures machinery used in broadacre, conservation tillage farming. The innovation, known as DOT™, is an entrepreneur’s response to problems in the agriculture industry. Applying the innovation opportunity space (IOS) conceptual framework, this study identified the process of innovation was based on synthesis of tacit knowledge (experience-based knowledge of farming and agribusiness) and codified knowledge (drawing on computer programming). The innovation offers a solution for farming problems, and other firms are incorporating the autonomous functionality into their short-line manufacturing operations through licensing agreements, and early farmer adoption is positive. However, this smart farming IOS is presently an Unstable IOS and there remain some gaps: public policy for safe deployment of autonomous agriculture vehicles is lagging behind the invention and commercialization; the new business models for manufacture and commercialization of high-tech equipment are just emerging, and data ownership and control remain unresolved; and evidence of the value of smart farming technologies to farmers and the larger social system and biosphere remains scant.

• Innovation opportunity space
• Smart farming
• Agriculture equipment
• Innovation policy

The twenty-first century is an exciting time for agriculture. The digitization of agriculture holds promise for meeting demands of increased food production ( Citation Tilman et al., 2011 ), addressing social concerns about animal welfare, enabling livestock traceability, and minimizing the environmental impact of livestock production ( Citation Busse et al., 2015 ) while building resilience and adaption to climate change ( Citation Adenle et al., 2015 ; Citation Searchinger et al., 2013 ). This new frontier of innovation in farming, becoming known as the smart farming future, is made possible through technological change involving continuous improvement in sensors, information and communications technologies (ICT), advances in data storage and analytics made possible with the Internet of Things (IoT), Cloud-based systems and ultimately the acceptance by farmers of digital technology tools for use on their farms.

Digital technologies create new possibilities for innovation, making it possible for today’s farmers to be more efficient, effective and economically successful than ever before while providing a new way of addressing persistent problems in commercial agriculture. What is noteworthy about these innovations is that they can also be seen as a broader response to changes in the economics and personal world around farming. For example, many smart farming technologies, including those classified by Citation Balafoutis et al. (2017) as bundled in agriculture equipment, provide the technological means for precise application of farm inputs, while improving farm management processes and supporting their license to operate. Automation of farm operations can further empower farmers to remain active on farms by addressing the critical constraint of severe labour shortages in the agriculture sector. Processes will be supported by the integration of wireless technologies and higher bandwidth enabling human-o-machine interactions (HMI), machine-to-machine (M2M) communication, and use of autonomous vehicles ( Citation Bacco et al., 2018 ).

Machinery-based applications of digital technologies are leading the way in ‘what’s next for agtech’ ( Citation Rogers, 2018 ). These developments also propel a radical shift in the manufacturing, retailing, intellectual property (IP) protection and servicing of agriculture equipment. Equipment is a major expenditure and represents a long-term commitment to using the new digital technological based innovations in agriculture. Is the investment by farmers ‘worth the money?’ ( Citation Tozer, 2009 ). Citation Bellon Maurel and Huyghe (2017) acknowledge the main impediment for new ‘high tech’ machinery is a deficit of demand at the farm level which is related to high investment costs, and real or perceived complexity of unknown technologies and while change in the mindset of farmers is crucial to an effective and sustainable smart farming production system, this may not be easy. They also assert agriculture equipment are a ‘set of resources’ to be mobilized, concluding this area of innovation is often misunderstood in the context of triggers for farmer’s decisions and adopting new technologies. We found few academic studies offering empirical evidence of smart farming innovations in equipment commercialized for use in large scale, broadacre agriculture systems. Our study aims to shed light on this topic and presents empirical evidence gathered from an equipment-based smart farming innovation.

The purpose of this paper is to identify opportunities as well as potential problems associated with innovations in smart farming technologies. Robotics, that is, autonomous equipment, was chosen for this research. The innovation named DOT™, is engineered for a smart farming future in broadacre agriculture systems and it is developed by a small-to-medium size enterprise (SME) agriculture equipment manufacturing firm based in Saskatchewan, Canada. A case study approach is used to answer the following research questions relevant to the current debate on innovations in agriculture.

(i) How are smart farming innovations enabled or limited by public policy, or advancing in the absence of governance models?

(ii) How might smart farming address problems at the farm level, while also reducing the environmental impact of crop production processes?

(iii) What are the potential risks associated with smart farming innovations?

The next four sections outline the scale and scope of the digital challenge and opportunity for a smart faming future under broadacre agriculture systems in Western Canada. We introduce and adapt Citation Flowers et al. (2017) Innovation Opportunity Space (IOS) framework to this problem area, apply the framework to study the emergence of an autonomous propulsion and navigation system for agriculture equipment designed and manufactured by an SME, and critically assess the challenges and opportunities of this innovation opportunity space.

Innovations must bring ‘dollars and cents’ value to the farmer before they become core to the conventions and culture of farming ( Citation Gray, 2010 ). We believe this is important to advancing our understanding of smart farming because many digital technology innovations in agriculture have not yet consistently proven their value in a farmer’s field through increased yields or reduced input costs, or ideally, both, and despite digital technologies used in precision farming having been available to farmers for several decades, adoption is lower than anticipated ( Citation Zhang et al., 2002 ; Citation Bramley, 2009 ; Citation Pierpaoli et al., 2013 ; Citation Mulla, 2013 ; Citation Mulla and Khosla, 2015 ). Over the long run, the use of precision agriculture (PA) technologies had “small positive impacts on both net returns (including overhead expenses) and operating profits” based on financial records collected from a representative sample of those declaring ‘corn farmer’ as a primary occupation in the United States ( Citation Schimmelpfennig, 2016, 6 ). Adoption varied across the PA technologies and farm size and field-level practices, including different levels of adoption, the type of digital technology and their costs (e.g. labour, machinery, training), must be taken into consideration in an economic analysis. The value of PA is site-specific ( Citation Griffin and Lowenberg-DeBoer, 2005 ), and Citation Schieffer and Dillon (2014) found the type of technology matters, particularly if its use is supported by public policies such as tax incentives, subsidies, or cost-share programs. Information analysis and decision support systems and digital technology based prescription-type recommendations for application of farm inputs such as variable-rate (VR) fertilization, have not been embedded in day to day farm operation, whereas digital technologies such as Global Positioning Systems (GPS) in agriculture equipment, autosteer, and accurate machine location technologies are widely adopted and have enabled “technological breakthroughs in precision farming” application ( Citation Mulla and Khosla, 2015, 24 ). In addition, autosteer is also now available for use on older machines ( Citation Booker, 2018a ).

Auto-guidance systems and automatic sectional control or ASC, combine to reduce costs by eliminating product overlap while providing precise application of seed, fertilizer, and chemical inputs and conferring a positive environmental impact by optimizing both nutrient and seed inputs ( Citation Bennett et al., 2016 ; Citation Ashworth et al., 2018 ; Citation Adams, 2013 ; Citation Smith et al., 2013 ; Citation Mulla, 2013 ; Citation Schieffer and Dillon, 2014 ; Citation Schmaltz, 2017 ). Auto-guidance technology is now well-established at the commercial farm level, regardless of equipment brand, with approximately 360,000 new tractors equipped with auto-guidance systems sold in 2015 and demand for 700,000 units is projected for 2028 ( Citation IDtechEx, 2018 ). In addition to the cost savings indicated above, auto-guidance systems ease fatigue experienced by equipment operators, improve driver’s reaction times and reduce mental errors ( Citation Bashiri and Mann, 2015 ).

Technological advances that offer automation of farm operations have not been widely adopted and many remain at a research level, while some are used on smaller-scale operations, and can improve worker safety ( Citation Shamshiri et al., 2018 ; Citation Hart-Rule, 2018 ; Citation Lyseng, 2017a ; Citation Manning, 2018 ). Agricultural robots have not been affordable on a commercial farming scale because of “high investment and maintenance costs” compared to available and inexpensive labour ( Citation Petersen et al., 2017 ), although this technology is likely to become more affordable in the future as labour costs increase.

The agriculture equipment manufacturing industry is changing rapidly, and according to Citation Zambon et al. (2019) even though advances in digital technologies make ‘smart farms’ possible, innovation remains limited to a few pioneering firms, notably larger enterprises (firms). The authors suggest equipment innovations by small size firms are limited by their research capacity to incorporate the latest advances into their product lines, yet they also suggest SMEs may have an advantage of flexibility in the path to future innovations in agriculture and enable ‘quicker reconfiguration’ in response to change in demand. We add to this discourse and conjecture that SMEs are well-positioned to shape smart farming innovations by providing a solution to a farm-level ‘problem’.

The dawn of the ‘fourth agricultural revolution’ or Agriculture 4.0 is upon us and is enabling new levels of innovation in agriculture through the use of “sensors, satellites-based precision, high-tech construction materials and digital technologies” (Carl-Albrecht Bartmer, 2015 in Citation Frankelius 2015, 19 ). The change is posited to transform farming, “the most traditional of traditional industries” and some suggest the changes are simply an extension of broader human behavioral evolution and the penetrance of digital technologies into the everyday lives of citizens as part of the ‘Great Rewrite’ of society ( Citation Brody, 2018 ).

In agriculture, the Great Rewrite is driving a radical shift in the agriculture equipment industry. Norms are evolving rapidly and three issues in particular are identified. The first issue, referred to as interoperability, is basic to a farmer's ability to incorporate multiple brands into his/her line-up of equipment and using what is adapted to their location, including use of different application programming interfaces (APIs). As firms integrated digital technologies and proprietary systems into their equipment, the lack of integration between brands, software, and signal interfaces became a constraint to communicating and transferring data between different onboard computers and was limiting farmers deriving full benefits from the precision and accuracy in equipment ( Citation Bochtis et al., 2014 ). Several approaches have been taken to address this constraint, but farmers, particularly in the United States, remain concerned. Examples of industry response include controlled area network (CAN) and Binary Unit System (BUS) for standardized physical connections between electronic components ( Citation Fountas et al., 2015 ). The International Standards Organization (ISO) standards were also created. ISOBUS 11787:1995 provided an Agricultural Data Interchange Syntax ( Citation Stafford, 2000 ) and ISO 11783-1:2017 is the worldwide serial and data network communication protocol for the agriculture industry enabling data communication between tractors, other implements, and farm management software ( Citation Freimann, 2007 ; Citation Cavallo et al., 2014 , Citation 2015 ; Citation Deere & Company, 2013 ; Citation International Standards Organization, 2019 ). Open-source software systems was another way to improve interoperability and one interesting example is the Ag Data Application Programming Toolkit (ADAPT) designed to transfer data from a farmer’s preferred API and display into different original equipment manufacturer (OEM) software systems ( Citation AgGateway, 2018 ). Several European Union (EU) value chain participants use ADAPT, including the OEMs owned by CNH Industrial N.V.™, and AGCO™ ( Citation Internet of Food & Farm, 2018 ). Despite these efforts to address interoperability, the ISO is a voluntary, not a compulsory industry standard, and while CAN-BUS and ISO 11783 accommodate standardization across implements, smart farming faces interoperability constraints including a universal operating platform ( Citation Manning, 2017 ).

Second, intellectual property (IP), in particular, copyright law, has entered into the realm of agriculture equipment manufacturing corporate strategies, which is challenging the traditional business model of explicit ownership, right to use, and agricultural equipment repair ( Citation Higgins et al., 2017 ; Citation Carolan, 2017a , Citation b ; Citation Lyseng, 2018a ). The OEMs position is that if anyone except an authorized dealership repairs equipment, the outcomes result in unsafe operation, the resale value is adversely affected, the capabilities and performance of the machine may be compromised and equipment may no longer be compliant with environmental regulations ( Citation Right to Repair, 2018 ). Meanwhile, farmers expect to continue the tradition of self-repair of machines they purchased without jeopardizing warranties. The issue is far from being resolved and IP and copyright control of agriculture equipment are especially contentious issues in the United States, with farm organizations in 17 states filing bills calling for ‘fair repair’ ( Citation Lyseng, 2018b ).

Lastly, central to the lure, and concerns, of smart farming as technological change poised to transform farming, is data. There is a myriad of sensors on agriculture equipment and anonymous industry sources suggest at least 240 sensors would be resident on a new combine harvester, and upwards of 60 sensors on a new, large tractor typical of broadacre farming. There are many potential points of failure and sensors needing repair, replacement, or attention. Furthermore, each sensor gathers data and provides a manufacturer with information on the functionality of farm equipment while diagnostic codes monitor machine performance and alert the user, dealership, and/or manufacturer of a problem. There is a particular concern that manufacturers track and gather information without the owner’s consent or knowledge that such data was being collected and transmitted from their machines ( Citation Janzen, 2017 ).

The industry has to date, however, embarked on a self-regulation approach to bring transparency to the collection and use of agricultural data. In the United States, farm organizations and industry stakeholders developed the Privacy and Security Principles for Farm Data. Firms which agree with the principles and pass an independent audit of their records will receive an Ag Data Transparency (ADT) certification ( Citation American Farm Bureau Federation, N.d. ; Citation Janzen, 2015 ; Citation AgDataTransparent, 2018a ). In New Zealand the dairy industry developed the Farm Data Code of Practice and the Code is now used in other sectors ( Citation New Zealand Farm Data Code of Practice, N.d. ). In Europe, the EU Ag Data Code is part of the General Data Protection Regulation ( Citation European Crop Protection, 2018 ) and is similar to the American farm data principles ( Citation Janzen, 2018a ). In all the above examples, the principles or codes are voluntary and while some equipment OEMS are participating, in general, industry participation is limited. As of December 2018, only 18 agribusiness firms are committed to the United States core principles for farm data and are ADT certified ( Citation AgDataTransparent, 2018b ). In the United States, there is pressure on governments to address the governance aspects of agricultural data ( Citation United States Senate Committee on Commerce, Science, and Transportation, 2017 ). Citation United States Senate Committee on Commerce, Science, and Transportation (2017) In Ireland, industry stakeholders are signaling action on data ownership is ‘urgently needed’ and an anticipatory governance approach is requisite to address industry concern ( Citation Régan et al., 2018 ). As a follow-up, Régan (2019) interviewed 21 key governance actors who acknowledged there is a need to strike a balance that protects farmer’s (data) rights without having strict rules constraining agriculture innovation and industry development.

For others, the digitization of agriculture is raising particular concerns about the ethics of smart farming, asking questions such as distribution of power and impact on human life and society ( Citation Carbonell, 2016 ; Citation Carolan, 2017a , Citation b ) and “sharing everything with the public at large” in order to better understand what is desirable for a smart farming future, and what is not ( Citation van der Burg et al., 2019, 9 ). Several authors focus on the need to examine the ‘data aspects’ of agricultural data which includes weather, agronomic, and machine data ( Citation Dowell, 2015 ; Citation Dowell and Ferrell, 2015 ; Citation Ferris, 2017 ). Issues include the lack of regulation and clarity in data holder’s rights and ownership, loss of trust in what agribusiness firms or other third parties are doing with farm-level data, insufficient anonymization of data to ensure personal privacy, access including use of data in civil or regulatory litigation, and the value proposition of data ( Citation Bronson and Knezevic, 2016 ; Citation Bronson 2018 ; Citation Wiseman and Sanderson, 2017 ; Citation Jakku et al., 2018 ; Citation Stroebel, 2014 ; Citation Ferris, 2017 ; Citation Trail, 2018 ; Citation Dowell and Ferrell, 2015 ; Citation Tatge, 2016 , and Citation Janzen 2017 , Citation 2018a , Citation 2018b ). Citation Carbonell (2016) adds to these discussions and questions the ethics of ‘big data’ ( Citation De Mauro et al., 2015 , Citation 2016 ), noting emerging power asymmetries and Citation Coble et al. (2018) further link big data with market-distorting activity. Citation Rose and Chilvers (2018) also raise questions about the ‘re-scripting’ phenomenon, particularly when it concerns the ‘directionality’ of innovation pathways and the capture of sustainable agriculture by ‘big emergent technologies’, at the expense of sidelining relevant stakeholders.

We found four relevant ex-post studies which provide evidence of attitudes and behaviours relevant to smart farming in broadacre systems. Citation Fleming et al. (2018) and Citation Jakku et al. (2018) interviewed 26 stakeholders from the grains industry in Australia. Smart farming is generating a binary debate between ‘big data – big farming’ which is concerned with data storage and regulations, and the ‘big data for everyone’ view, which is more concerned about rights and value capture by individual farmers ( Citation Fleming et al., 2018 ) . Interoperability, data storage and handling, and limitations in digital infrastructure are dominant technical themes identified by Citation Jakku et al. (2018) who documented industry concerns regarding data accuracy, reliability, transferability, and data fragmentation, noting that new skills will be needed for smart farming in Australia. Citation Pivoto et al. (2018) interviewed four agribusiness experts in Brazil, concluding smart farming tools target farm operations using a high level of technology. Firms are lagging in offering ‘simple and coherent interoperability’ between systems, services, and stakeholders, and much of the vast amounts of data being generated remains unexplored by farmers, hindered by limited telecommunications infrastructure in rural areas, lack of knowledge and level of farmer education. Bronson conducted 22 interviews with experts (technology designers) developing smart farming innovations for farmers in the United States and Canada, concluding “not all farmers are equally advantaged” (2019, 3). On one side of the ‘digital divide’ are designers of smart farming products and/or services that solve problems faced by rational, economic actors - farmers who are in the business of producing commodities. Design priorities are placed on “tightly controlled data collection and storage”, and securing data privacy (ibid, 4). In contrast, the activist group of designers develops open systems for farmers outside mainstream commodity production operations and smart farming technologies usable for a diversity of farming operations. These studies demonstrate some of the challenges on the horizon for a smart farming future. Our study adds to this body of literature and focuses on broadacre agriculture in western Canada.

The 2016 national agriculture census documented the types of digital technologies used by farm operators, persons responsible for farm management decisions, in Canada and the prairie provinces (Manitoba, Alberta, and Saskatchewan) where broadacre farming dominates production system ( Citation Statistics Canada, 2016a Table 32-10-0446-01 ). Four recent surveys supplement the government statistics and further elucidate the use of digital technology at the farm level and highlight hurdles and catalysts relevant to smart farming.

A voluntary e-survey was commissioned by Agriculture and Agri-Food Canada (AAFC) in 2017 and the 261 farmer participants operated an aggregate of nearly 405,000 ha of cropland in western Canada. GPS-based auto-guidance systems were used by 98% of the respondents, 80% used autosteer and 70% used ASC, and temperature and moisture sensor technology for monitoring stored grain ( Citation Steele, 2017 ). Citation Turland and Slade (2018) surveyed 514 Saskatchewan farmers and report similar use (94%) of GPS auto-guidance systems. Yield monitors and variable rate technology (VRT) are two yardsticks commonly used to measure PA adoption ( Citation Griffin and Lowenberg-DeBoer, 2005 ). In western Canada, Steele found about 50% of respondents had combine-harvesters with yield monitoring capability, although the technology is not always used, whereas Turland and Slade found greater use (75%) by Saskatchewan farmers. Variable rate technology (crop input prescription maps) was used by less than 50% respondents ( Citation Steele, 2017 ) and fewer than 30% was documented by Citation Turland and Slade (2018) .

Price is the greatest impediment reported by Steele, followed by weak communications infrastructure, lack of knowledgeable people to support farmers, the constant evolution of the technology, incompatibility with legacy systems, and mismatch with farmer’s needs. Farm Credit Canada (FCC) surveyed 2000 Canadian farmers in fall, 2018 ( Citation Farm Credit Canada (FCC, 2018a ) and similar to the AAFC data, farmers revealed uncertainty of benefits of PA. The same year, Stratus Research surveyed 700 farms (>800 ha) and the majority were in western Canada. Citation MacLean (2018) reported most respondents identified a ‘need for profitability’ and ‘better information for my farm’ as catalysts for use of digital technologies. All three surveys (AAFC, Stratus, FCC) reported concerns on ease of use and complexity of digital technologies, and data issues including data access, storage, and privacy, plus cybersecurity risks. These Canadian studies generally align with views from abroad (e.g. Citation Wiseman and Sanderson, 2017 , Citation Jakku et al., 2018 ; Citation Regan, 2019 ; Citation Kuehne et al., 2017 ).

Two additional aspects of digital technologies are highlighted for the Canadian context, trust and compensation for use of farm data. The FCC survey reported evidence of an erosion of trust as a hurdle for PA adoption, similar to two previous studies in the United States ( Citation Janzen, 2019a ). In comparison to FCC’s farmer survey in 2017, about 25% of respondents indicated they had ‘become less confident sharing their data’ due to concerns about data security, privacy, and transparency. The take-home message reported by Wall is that the Canadian industry as a whole has not earned farmers’ trust ( Citation Farm Credit Canada (FCC, 2018a ). On the other hand, there may be subtle distinctions among industry actors. Citation Turland (2018) examined farmers’ willingness to participate in a big data program, concluding Saskatchewan respondents were ‘most willing’ to share their data with university researchers compared to other industry actors and under conditions of positive or non-financial incentive. These studies demonstrate that our current state of knowledge about digital technology-based innovation in agriculture is suggestive of certain trends, however, research studies and voluntary surveys have not yet offered sufficient granularity to understand the many facets of smart farming. Our study begins to fill this gap.

Studying innovation in times of the Great Rewrite is a challenge. As suggested by Citation Wolfert et al. (2017) , the technological change in agriculture is moving rapidly. By the time research is completed, the dynamics of the industry and the innovation have changed. Smart farming is a relatively new concept and in absence of new approaches to study digital transformations in agriculture, or analytical and/or conceptual frameworks suited to a study involving novel business models for agricultural equipment, traditional frameworks used to research agriculture innovation were considered for this study including, for example, the Tidd and Bessant model for how firms manage innovation ( Citation Ferreira et al., 2015 ) and New and Emerging Science and Technologies framework ( Citation Robinson et al., 2013 ). The Agriculture Innovation Systems (AIS) conceptual framework is useful to understand the complexities of the system across multiple levels of actors ( Citation Klerkx et al., 2012 ). It is used to study established innovation systems, co-innovation projects, or open systems such as those reported by Citation Borremans et al. (2018) ; Citation Klerkx and Nettle (2013) ; Citation Schut et al. (2016) ; and Citation Turner et al. (2017) . The Responsible Innovation (RI) or Responsible Research and Innovation (RRI) framework approaches are based on the prospective notion of responsibility and promoting a diversity of views to ‘proactively anticipate’ outcomes of research and innovation ( Citation Eastwood et al., 2017a ). RI considers four basic principles: anticipation, reflexivity, inclusion, and responsiveness ( Citation Stilgoe et al., 2013 ) and is used to study smart farming innovation (e.g. Citation Long and Blok, 2018 ; Citation Regan, 2019 ; Citation Jakku et al., 2018 ; Citation Bronson, 2018 ).

The innovation featured in this case study is not a good fit with any of the above frameworks for the following reasons. DOT™ is manufactured by a private corporation, business information is confidential, and it is neither a product of co-innovation nor an open system. The innovation system is just beginning to take shape and few, if any farmers, researchers, or government decision-makers have prior experience with the innovation or the new space being created. Furthermore, this case is not a normative study, nor it is intended to prescribe what an innovation system ‘should be’. Instead, the study is intended to support theory-building ( Citation Eisenhardt and Graebner, 2007 ) and provide evidence to advance policy-making.

The Innovation Opportunity Space (IOS) framework is a new conceptual framework developed Citation Flowers et al. (2017) and is for the first time being applied to smart farming innovation. The IOS is designed to “provide strategic managers, entrepreneurs, policymakers and academics with an improved way of viewing innovation-related issues” and was used to study Kickstarter, Airbnb, and Uber, open data projects, and a community forestry strategy in Finland ( Citation Flowers et al., 2017, 9 ). The analysis starts with firms and a focus on products of innovation and takes a “more neutral starting point” than other frameworks: “the space [own emphasis] into which an innovation will be introduced” (ibid). The IOS framework also incorporates an anticipatory aspect based on empirical evidence which is then used to map out one of many potential future pathways of innovation. Three main features of the IOS framework make it well suited for this research.

Table 1 Types of Innovation Opportunity Spaces.

Challenges for use of the IOS framework were operationalization, and as noted by Citation Buheji and Ahmed (2018) in their review of the IOS book, there was little guidance in methodological tools. In this study, Actors and Activities are reported as one section of the results and the following case study methodology was developed and discussed in the next section. Supplementary information is provided in the Appendix.

7.1 Case description

A case may be an object or an organization, which in turn establishes boundaries for data collection ( Citation Yin, 2009 ). In this research, the focal unit of analysis is an innovation named DOT™, the case is the totality of the IOS, and the case boundary is defined as follows: (i) equipment used in broadacre farming on the western Canadian prairies (ii) advanced equipment manufacturing in Canada; (iii) smart farming technology used in agricultural equipment, and (iv) the timeframe for data collection, July 2017 to December 2018.

Approximately 46% of Canada’s total farms and 45% of Canadian farmers are in the three prairie provinces of Manitoba, Alberta, and Saskatchewan ( Citation Statistics Canada, 2016b, Table 32-10-0440-01 ), where the bulk of Canada’s grains, oilseeds, pulse, forage crops, and the majority of Canada’s livestock exports originate ( Citation AAFC, 2017 ). Prairie farms are large, hence the term broadacre, with 26% cultivating more than 1425 ha, and some larger than 10,000 ha ( Citation Statistics Canada, 2016c, Table 32-10-0156-01 ). Farm area is about 64.2 M ha ( Citation Statistics Canada, 2016d, Table 32-10-0153-01 ) and in 2016 there were about 12,300 farm operators, of which 55% are aged 55 years and over, 35% were 35–54 years and 9% were less than 35 years ( Citation Statistics Canada, 2016e Table 32-10-0442-01 ). About 52% of Canadian farms are under sole proprietorship business arrangements; 23% are family corporations; 17% are a partnership without a written agreement, and less than 3% are non-family corporations ( Citation Statistics Canada, 2016f, Table 32-10-0433-01 ). Agriculture equipment is the single largest (65%) capital cost for prairie farmers and in 2016, machinery and equipment accounted for approximately 12% (or CA$ 35.17 B) of total farm capital ( Citation Statistics Canada, 2016g, Table: 32-10-0437-01 ). In 2017, fuel costs accounted for approximately 6% (or CA$ 1.48 B) of total farm operating expenses (about CA$ 24.68 B) and cash wages, room and board represented 7% (or CA$ 1.78 B), of which 51% is non-family wages ( Citation Statistics Canada, 2016h, Table 32-10-0049-01 ).

Large original equipment manufacturing firms have historically not been interested in research and development (R&D) for equipment used in a relatively small market such as the prairies ( Citation DeRyk, 1991 ; Citation McInnis, 2004a , Citation 2004b ) and particularly, farming under extreme climatic conditions and highly variable soil types ( Citation Bueckert and Clarke, 2013 ; Citation Padbury et al., 2002 ). Consequently, innovations in farm equipment suited for use in broadacre dryland agriculture were often invented and manufactured by SMEs directly situated in the agriculture region of the North American Great Plains. This has created a vibrant industry and in Saskatchewan the firms are particularly strong in the manufacture of air seeders used for conservation tillage, precision GPS technology, and advanced spraying systems ( Citation Saskatchewan, 2016 ). The Saskatchewan Manufacturers Guide, a voluntary registry of agricultural equipment manufacturers, currently lists 164 self-declared companies ( Citation Saskatchewan, 2019 ). One of these is SeedMaster, an SME and family-owned and managed, private corporation that employs about 80–100 staff, and is located in Edenwold, Saskatchewan.

The innovation featured in this study, DOT™, took three years to develop and was revealed at an outdoor farm event in Saskatchewan, Canada, July 2017. The creation, production, and assembly of DOT™ is done by Dot Technology Corp.™ which was formed as a sister company to SeedMaster ( Citation SeedMaster, 2018a , Citation 2018b ). The initial aim for DOT™ was the production of 30-foot (9.14 m) units sized for North America, Eastern Europe and Australian markets ( Citation Raine, 2017 ). After prototype evaluations on the SME’s research farm, field testing was done in spring 2018.

DOT ™ is a smart farming innovation that may be conceptualized as both a physical and virtual system. Visually, the innovation is represented as: a 12-foot high (3.66 m) U-shaped DOT Power Plaform™ platform or frame, black in colour with stainless steel accents, weighing approximately 5,570 kg and powered by a 163-horsepower (HP), 4.5 L Tier 4 emission standards Cummins diesel engine with 320-litre fuel capacity ( Citation DOT-TechSpec Sheet, 2018 ; Citation seedotrun.com, N.d. ; Citation Garvey, 2019 ). DOT™ is also a platform for a virtual operating system hosting a suite of sensors and wireless technologies that support automation of farm equipment described by: Citation Bacco et al. (2018) ; Citation Adams (2013) ; Citation Carballido et al. (2014) ; and Citation Balafoutis et al. (2017) . Once loaded with the specific piece of farm equipment, it ‘becomes one’ with the DOT Power Plaform™ ( Citation DOT-TechSpec Sheet, 2018 ; Citation AgDealer TV., 2017 ).

DOT™ operates based on a prescribed, pre-programmed path plan that is quickly generated by software, typically less than 15 s. The path is unique for each field (or yard site), obstacles to travel are mapped with sub-inch accuracy, and the plan must be approved by the user. Guidance and navigation system intelligence sense distinct boundaries, and DOT™ powers down when boundaries are violated ( Citation DOT-TechSpec Sheet, 2018 ). Sensors continually analyze slippage and mud-sinking, which, if the pre-selected limits are triggered, this will also stop DOT™. In full autonomous mode, DOT™ can travel from a slow creep, up to 19 km per hour and sensors controlling the position-sensing hydraulic cylinders steer DOT™ to deliver four-wheel turning under full power. Handheld remote manual control is used to load the platform onto a trailer for transport between the field and farm yard site as DOT™ does not travel on public roadways.

8.1 Architecture

The first element, Architecture, considers the following aspects: the cultural context and a problem identification perspective as a root cause for the inventor creating a solution to dilemmas in Canadian agriculture; the technological context, specifically the smart farming technologies bundled in the innovation and the equipment manufacturing capacity of SMEs; market context with analysis on market size and structure, number of firms, and in this case, the tractor market for which the innovation may disrupt; and the policy context, which examines government regulatory structures.

8.1.1 Cultural context: Canadian agriculture dilemmas

Labour shortages are a persistent problem in the agriculture industry ( Citation Canada, 2002 ). The Canadian Agriculture Human Resource Council ( Citation Canadian Agricultural Human Resources Council (CAHRC, 2016a ) estimates 26,400 jobs went unfilled in 2014, which cost the agriculture sector CA$ 1.5 B in lost revenue. In 2018, this value had increased to CA$ 2.9 B in lost sales ( Citation Canadian Agricultural Human Resources Council (CAHRC, 2019 ). Labour shortages doubled from 2006 to 2016, the shortages are expected to double again in the next decade and CAHRC projects the labour problem will intensify by 2025 ( Citation Canadian Agricultural Human Resources Council (CAHRC, 2016b ). Recruitment barriers for attracting people to work on prairie farms include the rural location, lack of workers with required skills and experience ( Citation Canadian Agricultural Human Resources Council (CAHRC, 2014 ). The aging workforce, rural location, negative perceptions of agriculture and seasonality of employment, are other factors driving the growing labour gap ( Citation Conference Board of Canada, 2016 ).

We want to make labour obsolete because there is no labour for us [participant emphasis]. Many people just don’t understand this (Interview participant)

[A]t one end of the scale you get the young, 35 to 40-year-old farmers who really want to get going with anything new and high tech. … but at the other end, we have farmers who are 80 years old saying they’re too old to get up and down from the tractor, but they still have a passion for agriculture. This would give them a way of still utilizing their brains and less of their brawn (Norbert Beaujot, Alberta Farmer Express, February 26, 2018).

Big farms want new equipment lines, leases. The auction marts are carrying big inventories but with a high price tag on these machines, they are still not affordable for the average to smaller farm and younger operators ( Citation Beaujot, 2017a ).

DOT™ works to descale the industry and re-imagines retailing of equipment. One unit, paired with a 9.15 m air seeder, for example, will efficiently seed a 1400 ha farm (the same land area that is typically harvested by a single combine harvester). With an investment of CA$ 500,000, a farmer would be able to purchase the DOT Power Plaform™, a seeder with four product tanks (seed, fertilizer, pesticide, fungicide), a sprayer and a grain cart for carrying the seed, fertilizer, etc. Larger farms would use multiple DOT™ units ( Citation DOT-TechSpec Sheet, 2018 ).

8.1.2 Technological context

Multiple layers of remote-sensing technologies required for the safe operation of autonomous vehicles are incorporated in DOT™, including cameras, radio detection and ranging, and Light Detection and Ranging (LIDAR) technology. Remote human-to-machine interface (HMI) consists of: HMI sensing and display of engine performance, HMI implement remote control and recording, and HMI long-range Wi-Fi and radio connectivity which accommodates geo-referencing of applied variable rate mapping and monitoring of fuel usage and power draw ( Citation DOT-TechSpec Sheet, 2018 ). Machine-to-machine communication is being developed to prevent accidents and collisions in the field. Implement controls for DOT™ follow the ISOBUS 11783 protocol, providing a base level of interoperability.

The ‘easy to operate’ user interface (Windows Surface Pro Tablet) talks to DOT™ through a local area network (LTE WAN) with Real Time Kinematic (RTK) base stations. All DOT™ owners and users are required to attend the training sessions provided by Dot Technology Corp.™ and have hands-on experience before they operate DOT™ in a field ( Citation seedotrun.com, N.d. ). All technology developed in-house is proprietary. Although not explicitly articulated in the interviews, at a minimum one would expect the software will have copyright protection. That being said, DOT™ management foresees the industry generally moving to a brand or software agnostic approach. The primary driver is efficiency and users of portable computer alternatives are familiar with the Tablet interface.

The real challenge comes around the cell network and its ability to process high amounts of data. … because it’s not reliable, we’ve got to set up a secondary long-range WiFi network that is a cost that’s borne by the farm. … future generation 5 G network would make a big difference, but again that will first surround urban areas, so that’s where the challenges lie. … It’s the coverage and capacity, pure bandwidth, the amount of data that truly needs to be transferred for successful autonomous and long-range telematics. It either needs to have its own network constructed within a closed environment, or we need to have a better more reliable system…. that’s Precision Ag, it’s driverless cars, it’s a much greater issue than just what we’re doing (Interview participant).

A high-power, high-range Wi-Fi network comes as part of the DOT package with SeedMaster essentially acting as the Internet service provider… the user app will be hosted on a web server through which the farmer will access DOT. … We’ve proven we can communicate with DOT up to 15 km through our local area network, but the price is higher for that capability (Owen Kinch, reported by Citation Melchior, 2018 ).

[i]n the electronic portions of it, we have to be cautious that they aren’t meddling with something that could affect safety or machine health. … [w]e don't want them fixing a radar sensor or something like that. … most of those things are just - unplug - plug a new thing in - and go. You’re not going to open a radar sensor and try and fix it. I wouldn't anyway! But we definitely will be more friendly than other OEMs with that aspect of it, as long as we can record who did what and when (Interview participant).

We would definitely have it [data aspects] as part of the agreement with the farmers, that we're allowed to view certain parts of the data that deal with machine health in particular (Interview participant).

8.1.3 Market context

An autonomous propulsion system may reduce reliance on pull-type systems (tractors) to complete farm operations and potentially shift manufacturing and export markets. As there is currently is no market established for autonomous agricultural equipment, analysis is limited to the tractor market which DOT™ could potentially disrupt, the capacity of SMEs to fill the space created by this disruption, and farmer interest in autonomous equipment. The big question is, how large is the potential market for such an innovation?

The International Trade Centre (ITC) reports world exports for tractors with engine power more than 130 kW (kW) or about 174 HP, the largest size category that is classified using the new harmonized standard (HS) code 870195. World exports of HS 870195 in 2017 were US$ 5.8 B and Germany and the United States dominate the market, together accounting for 54% of global trade exports ( Citation International Trade Center (ITC, 2018 ). At the manufacturing level, about 24 firms make large tractors and approximately 5,100 models are listed in the TractorData.com database ( Citation Tractor Dat, 2018 ), however, the following five major manufacturing firms lead world markets: John Deere™, AGCO™, CNH™, SAME™, and Zetor™. John Deere™ holds a dominant position in North America, selling 245,000 tractor units in 2017 ( Citation Tractor and Combine Sales, 2018 ).

Autonomous tractors made have not yet reached commercialization by the OEMs ( Citation Allen, 2018 ; Citation Case IH, 2016 ; Citation New Holland, 2016 ) and while IDtechEx (2017) estimates the value of the autonomous tractor market to be around US$ 27 B, it will still be about five years (2024) before the market changes as regulations, high sensor costs and lack of farmer’s trust are constraints to large-scale market introduction. Nonetheless, Beaujot suggests that any farming community that has the expertise to do precision farming and has a labour problem is a potential market for DOT™ autonomous technology ( Citation Beaujot, 2017b ). Furthermore, any equipment manufacturer can adapt their products to the platform and manufacture their line of ‘DOT Ready™’ autonomous, tractor-less, farm implements once they participate in the licensing model offered by Dot Technology Corp.™ ( Citation DOT-TechSpec Sheet, 2018 ).

Canada ranks among the world’s top agriculture equipment manufacturers and in 2017, Canada exports totaled CA$1.98 B with sales to 154 countries ( Citation Canada, 2018a ). Ninety-two percent of the 535 equipment manufacturers are categorized as SMEs, firms employing less than 100 staff, and average revenue earnings of CA$ 1.0 M ( Citation Canada, 2018b ). The SMEs utilize digital technologies in advanced manufacturing processes, including Computer-aided Design (CAD), laser cutting, additive manufacturing described by Citation Levy (2010) , and are incorporating artificial intelligence in their products ( Citation Binkley, 2018 ). Four Saskatchewan SME-made, DOT Ready™ autonomous implements are available in spring 2019, including a conservation tillage seeder, a sprayer, a dry (fertilizer) spreader, and a grain cart ( Citation seedotrun.com, N.d. ).

[a] brand-new tractor is $700,000 and a brand new, great big seed drill can be in the $700,000 range - that's $1.4 million to seed your crop. … If you can buy a less-expensive robot that can run longer and save you some time, to me, it's a no-brainer ( Citation Melchior, 2018 ).

In a press release reported by the SME, farmers had, by spring 2018, reserved and paid deposits on most of the projected production for 2019 and 2020 (Dot Technology Corp.™ e-News subscribers, 2018).

8.1.4 Policy context

The agricultural equipment market is governed through a combination of codes, standards, and guidelines set for manufacturers of goods or services for use in domestic and international markets. Canadian firms use manufacturing standards recognized by the ISO, emissions controls set by the American Society of Agricultural and Biological Engineers, as well as safety, health, environmental, and quality of life standards established by the Canadian Standards Association ( Citation Agricultural Manufacturers of Canada, 2018 ).

At the provincial jurisdictional level, the provinces of Saskatchewan, Alberta, Manitoba, and Ontario have legislation prescribing the relationship between agriculture equipment manufacturers and dealers ( Citation Agricultural Equipment Statutes, 2019 ). These primary statutes provide the legal framework requiring equipment dealers, manufacturers, and distributors to supply repair parts for a period up to ten years following the date of sale on a new machine sold in the province, and in addition, make these repairs available within a specified time period, for example, an emergency repair during critical use periods such as seeding or harvest must be done within 72 h in Saskatchewan ( Citation Garvey, 2015 ). If these conditions are not met, a farmer may file a compensation claim to an oversight body appointed by the government executive council, cash settlements may be imposed on a dealer or distributor and a penalty fee awarded to the farmer as compensation. As of May 2019, it has not been confirmed whether autonomous agricultural equipment will be subject to these provincial regulations.

The other main policy area relevant to autonomous agricultural equipment is liability insurance schemes for autonomous vehicles ( Citation Yeomans, 2014 ; Citation Janzen, 2019b ). Motor vehicle transportation is a complex policy area and in Canada, efforts are underway to develop industry standards for autonomous vehicles. Federal, provincial, and territorial governments have shared jurisdiction, and multiple government departments are involved. The Canadian Council of Motor Transport Administrator (CCMTA), a multi-stakeholder group, sets voluntary guidelines for the safe testing and deployment of highly automated driving systems ( Citation CCMTA, 2018 ). Transport Canada is responsible for research and public education, setting safety standards for manufactured and imported vehicles, and enforcing compliance. Innovation, Science and Economic Development Canada governs technical standards, addresses data and intellectual property issues, and supports R&D investment. The provinces/territories are responsible for adapting infrastructure to support autonomous vehicle deployment, licensing, registration, safety inspections, insurance and liability (ibid, 20). Authority for enacting and enforcing bylaws on local roadways resides at the municipal level. According to the CCMTA, Canadian guidelines for autonomous driving systems are ‘in-scope’ for vehicle registration, driver training/licensing, and enforcement of traffic laws. However, the CCMTA reports that several areas remain ‘out-of-scope’ including safety programs and criteria, data privacy and security, enabling infrastructure, and cybersecurity (ibid, 15). That being said, there is neither classification for ‘farm vehicles’ and associated regulations for motor vehicle safety, nor are there are Canadian guidelines for autonomous agriculture vehicles ( Citation Garvey, 2018 ). It has been generally assumed that such vehicles would not travel on public roadways. This may not always be the situation in the future if autonomous farm vehicles move between farm fields and yard sites connected by rural access roads, in which case an e-tether system with a lead automobile could be an effective transport mechanism and one that is being developed by Dot Technology Corp.™.

8.2 Actors and activities

8.2.1 Agriculture equipment manufacturing

[T]oday’s agriculture has changed. We have 100-foot seeders. SeedMaster is good at this but is bigger better? We have created our own problems - compaction concerns, lost time filling, wide turn radius and lack of buyers for equipment (Interview participant).

Initially, Beaujot’s ‘idea napkin’ illustrated a single-purpose unit (a self-propelled seeder), then after recognizing “there were too many wastes associated” with that approach, he designed the ‘open U’ concept platform capable of handling many types of implements (Norbert Beaujot, adapted from the interview transcript, 2017). The ‘Farming Reimagined’ idea became a reality. DOT™ is manufactured at the SeedMaster facility and the SME provided R&D capacity, engineering talent, market expertise, and boot-strap financing for DOT™, investing $1.6 M into the prototype ( Citation Saskatchewan, 2018 ). Field-testing DOT™ with multiple implements was conducted at the SeedMaster research farm. Ideation to the creation of DOT™ was a three-year process beginning in 2014 and several resources were mobilized in the process.

[T]hey were displaying at shows and we went to talk to them …. They're Saskatchewan boys- I don't know if they all have farming in their roots or not… they are just guys who like making things move by themselves. I'm sure they have or will continue to, gain more and more appreciation for the ag world, but they're coming into it from a very pure kind of programming robotic headspace. I think that's been great though. They've been very receptive and thinking about it from a different perspective than perhaps a farmer would (Interview participant).

SeedMaster and Dot Technology Corp.™ are members of the industry association, Agricultural Manufacturers of Canada (AMC), a non-profit organization with a purpose to “foster and promote the growth and development of the agricultural equipment manufacturing industry in Canada” ( Citation Agricultural Manufacturers of Canada, (AMC, 2018 ). Headquartered in Regina, Saskatchewan, AMC has an advocacy role and coordinated funding for R&D on advanced manufacturing technologies. In spring 2018, the president of the AMC, Ms. Leah Olson, was hired as Dot Technology Corp.™’s Chief Executive Officer ( Citation SeedMaster, 2018a , Citation 2018b ).

To access additional technology beyond the existing capacity of SeedMaster, Dot Technology Corp.™ entered into a strategic partnership with Raven Industries ( Citation McIntosh, 2018 ). Raven brought decades of expertise developing technology for space exploration and PA ( Citation Raven, N.d. ) and became providers of mobile hardware and software custom-designed to control DOT™. Cummins, a manufacturer of engines commonly used to power agriculture equipment, brought a century worth of expertise in engine manufacturing, plus expertise in autonomous technology ( Citation Cummins, 2019 ; Citation Garvey, 2019 ). Cummins worked with Beaujot to supply the diesel engine that powers DOT™. The engine meets Tier 4 emissions standards described in the United States Citation Environmental Protection Agency (EPA) Regulations (EPA, 2019) .

8.2.2 Government

The next group of actors is government and activities directly related to the financial support of R&D in the manufacturing sector. The National Research Council of Canada’s Industrial Research Assistance Program (NRC-IRAP) administers the Youth Employment Programme (YEP). The YEP supports skill development in the manufacturing industry and offers wage subsidies for firms to hire the talent they need ( Citation Canada, 2016 ).

At the provincial level a relatively new policy, the patent box tax incentive, is offered by the province of Saskatchewan through the Patent Box Act , 2017, and according to Citation Gowling WLG (2016) , the Patent Box Programme positions the province with a major policy-leadership position for improving Canada’s innovation competitiveness. The programme is implemented with co-operation from NRC-IRAP and is available to firms headquartered in the province that create new jobs through R&D done in Saskatchewan ( Citation Saskatchewan, 2017 ). An official announcement was not made when Dot Technology Corp.™ qualified for the programme, however, government sources confirm that DOT™ qualified for the innovation incentive. About one year after DOT™ was first revealed at a farm industry show, the government announced it would provide direct financial support to further the development of autonomous functionality for use in the agriculture sector. The Saskatchewan Advantage Innovation Fund (SAIF) committed CA$ 230,000 to Dot Technology Corp.™ in support of collaboration with University of Regina researchers and further develop the tablet device for improved user interaction ( Citation Saskatchewan, 2018 ).

Beyond early-stage financial support, government’s role in the DOT™ IOS has mainly been indirect such as sponsorship for organizations to host information exchange events including farm industry trade shows and conferences. Some of the largest information exchange events are organized by farm media groups.

8.2.3 Farm news media

Table a1 trade shows selected as sites for study., table a4 farm media as secondary data sources disseminating information on dot™ and dot technology corp.™, july 2017 to december 2018..

Ag in Motion is Western Canada's Outdoor Farm Expo, hosted by Glacier Farm Media. The event allows farmers to ‘get up close and personal’ with technology ( Citation Ag in Motion, 2017a ). Farmers can ‘be empowered’ by the new agricultural technologies, including interacting with inventors and manufacturers, observing the performance of innovations in real-time under field conditions, and comparing equipment brands ( Citation Ag in Motion, 2017a ). At the July 2017 Ag in Motion event, DOT™ was first revealed to about 23,000 event attendees including farmers, government ministers and staff, and the general public. Innovations at Ag in Motion are peer-judged and in October 2017, event organizers announced that DOT™ had won two awards, Innovation in Agriculture Equipment Technology and the People's Choice Award ( Citation Ag in Motion, 2017b ).

The Western Canadian Farm Progress Show is one of the largest farm industry events and the June 2018 event was sponsored by Farms.com ( Citation Canada’s Farm Progress Show, 2018 ). The venue encourages competitiveness in the agriculture sector and innovations are evaluated by a panel of expert (industry) judges ( Citation Farm Progress Show, 2018 ). Among 22 submissions evaluated in 2018, Norbert Beaujot and Dot Technology Corp.™ received the highest ranking, the Gold Standard Innovation Award recognizing DOT™ ( Citation Briere, 2018b ).

With Africa on the precipice of its own agriculture revolution technologies like these could speed up the process of putting the continent’s 60 percent of the world’s uncultivated arable land to use ( Citation Vanek, 2018 ).

8.2.4 Farmers

The final group of actors is farmers as consumers of smart farming innovations. Farmers observed DOT™ at trade shows, engaged with the Dot Technology Corp.™ management team at the venues, attended SeedMaster Master Seeder workshops, commodity group meetings and town-halls (2017/2018), and read about the innovation in the farm news.

Early indications are that farmers are not opposed to autonomous agriculture equipment, and in fact, farmers are making their equipment autonomous. For example, a Manitoba farmer made his tractor autonomous out of necessity to pull a grain cart at harvest time when labour was not available ( Citation Hackaday.io, N.d. ). Tractors can be made autonomous using AgOpenGPS mapping software for units equipped with CAN-BUS and much of the necessary information is found through on-line farm hacker forums ( Citation Booker, 2018b ).

After DOT™ was first demonstrated at Ag in Motion, Glacier Farm Media conducted a post-event survey of their subscribers, finding 1% of approximately 400 respondents have fully adopted autonomous vehicles on their farm; 3% were actively testing autonomous vehicles; 10% expected they would be ready within two years, and 70% ranked it as a low priority ( Citation Glacier Farm Media, 2017 ). Over half indicated time savings would be the main benefit, but that the future of autonomous technology hinged on rural labour shortages and 45% indicated budget constraints as the top barrier to adoption of autonomous farm implements ( Citation Lyseng, 2017b ).

8.3 Aftershock

The Aftershock element of the IOS is defined as the “impact and outcomes of the actions taken place by the actors within the opportunity space” ( Citation Flowers et al., 2017, 209 ) and this paper focuses on three aspects of the aftershock – the SME community, the economic and environmental impact of the IOS and government policy. At this time, the main impact is the uptake of manufacturing DOT Ready™ implements and the decreasing time from design to field testing of a new implement with autonomous functionality.

8.3.1 A new agricultural equipment manufacturing space

It’s very difficult for a small company to be able to build all of the automation that needs to go into it [product development], so for a short-line smaller manufacturer like ourselves, being able to partner with the DOT™ folks; it's amazing because we can collaborate and collectively build something where individually we would not probably do that (Rick Pattison, president of Pattison Liquid Systems and Connect, July 5, 2018, as quoted in Citation Heppner, 2018 ).

A second short-liner has now entered into the DOT™ IOS. New Leader Manufacturing, based in Cedar Rapids, Iowa, has been producing equipment for over 80 years, and operations have expanded with an international facility established in another broadacre agriculture area, Brazil ( Citation New Leader Manufacturing, N.d. ). They began production of a DOT Ready™ line of line of fertilizer spreaders, adding autonomous functionality to their product line-up of fertilizer spreaders traditionally mounted on chassis for OEMs including John Deere™, Case IH™ and AGCO™ ( Citation Booker, 2019 ). The

At the July 2019 Ag in Motion event, the Dot Technology Corp.™. management team summarized the scale of testing and uptake of DOT™. Several early adopters are using DOT™ and Beaujot told the audience gathered for demonstration of the three pieces of autonomous equipment that approximately 5,000 acres (2,023 ha) were planted using a DOT Ready™ SeedMaster no tillage seed drill, while the CONNECT™ PLU has sprayed over 11,000 acres (4,451 ha). Dot Technology Corp.™ has also been working with the Saskatchewan and Alberta governments to develop safety regulations for use of autonomous farm vehicles ( Citation Relf-Eckstein, 2019 ).

8.3.2 Farm-level value and impact on the biosphere

The highly anticipated benefits derived from the DOT™ IOS are hard to quantify. Estimating the impact of DOT™ at the farm level is particularly challenging as individual farm operator data is reported in aggregate by Statistics Canada. Information is not cross-referenced to a piece of equipment, operation, or task, and the actual cost savings to farmers will vary based on farm size, wage rate, and type of farm operation. The following impacts and outcomes are therefore maximum theoretical estimates based on available government statistics.

[I]t’s a double-edged sword. You have to then train the workforce; you have to adapt to those new production techniques and technology, and in order to maintain those systems; you need different skills moving forward (MacDonald-Dewhirst in Citation Blair, 2019 ).

Aftershock on the biosphere is linked to reduced fuel emissions and usage, and improved soil health. The new Cummins engine with Tier 4 emission standards powering DOT™ could displace older (fuel emissions) technology tractor engines with less stringent environmental regulations. Reduced fuel costs are related to the decrease in (horse)power required per acre from less weight, and the basics of a propulsion versus pull-type system. A fully ballasted 400 HP tractor, a typical size used to pull the standard-size planting and tillage equipment on broadacre farms, weighs approximately 18,100 kg. In comparison, a DOT™ unit offers a nearly 70% reduction in weight as ballasts, wheels, drawbar, hitches, and folding apparatus are no longer required. As explained by Beaujot, a comparable ballasted tractor requires between 20 and 30% more horsepower than a DOT™ unit, thus requiring between 20 and 30% more fuel. Using fuel cost statistics aggregated over the three prairie provinces, the impact would equate to a farm-level fuel cost savings of CA$ 295 M ( Citation Statistics Canada, 2016h Table 32-10-0049-01 ), equal to CA$ 19.90 per hectare, based on the 2016 area of 27.05 M ha, not including pasture land area. In addition, with a DOT™, the weight traveling the field is substantially reduced and this may have an impact on soil compaction. Soil compaction caused by heavy tillage equipment is recognized as “one of the most severe degradative processes in mechanized agriculture” ( Citation Blanco-Canqui and Lal, 2008 , 402). Soil chemistry changes with compaction, impacting the mobility of elements, biotic activity of roots and earthworms ( Citation Whalley et al., 1995 ). With pore space compromised, the increased anaerobic conditions can lead to higher production of methane, a greenhouse gas ( Citation Nawaz et al., 2013 ). Change in soil structure also affects physical processes of the soil, reduces root and shoot plant growth and crop yield and impedes water infiltration, which in turn increases runoff of water, nitrates, and pesticides into groundwater ( Citation Soane and van Ouwerkerk, 1995 ; Citation Hamza and Anderson, 2005 ).

Measuring the impacts and outcomes on the biosphere is not an easy task and beyond the scope of the SME to quantify. Some suggest the impact on the biosphere may be larger than the fuel savings, although long-term experiments will be required to understand the environmental aftershock. Such experiments could include, for example, head-to-head testing of DOT Ready™ implements with tractor-based systems, measuring the environmental effect using different land management practices, and testing on farms representing a diversity of soil types and structures, climate and weather conditions.

8.3.3 Government policy and changes in the global supply chain

Four policy areas with direct relevance to the DOT™ IOS are identified in this study. The first relates to Canadian policy on skill development. The impact of the YEP was hiring and experiential learning for university students with computer programming talent and a passion for robotics but minimal knowledge of agriculture. The locally-trained students worked with Beaujot and the SeedMaster manufacturing team who had tacit knowledge of farming, equipment manufacturing and retailing and a deep understanding of the broadacre agriculture industry. The outcome was co-creation (synthesis) of new knowledge and diversifying R&D capacity in Dot Technology Corp.™ The trained students became staff of Dot Technology Corp.™ and have been active participants in the talent search process as production scales up, and are brand ambassadors for new ideas/uses of autonomous technology ( Citation AIMday, 2018 ).

The second policy aftershock relates to innovation platforms. At the national level, boosting innovation in agriculture is viewed as a key to productivity growth in Canada ( Citation Economic Council of Canada, 2017 ). The federal policy, the national Innovation Supercluster program, is part of Canada’s economic strategy ‘flagship’ policy platform with a budget commitment of CA$ 950 M supporting advanced manufacturing, agri-food, health/bio-sciences, clean technology, and digital industries ( Citation KPMG, 2018 ). Dot Technology Corp.™ is part of the Innovation Supercluster (ibid, slide 11). The SME’s role in the Supercluster was not disclosed during the interviews, although the inclusion of Dot Technology Corp.™ in the Supercluster may signal Canadian government interest in multi-sector autonomous equipment innovation. In February 2019, the federal government announced funding for precision farming under the Canadian Agricultural Strategic Priorities Program (CASPP). The recent policy objective is to “develop and deliver large-scale, disruptive approaches to automation and digital technology with applications within the agriculture and agri-food sector value chain” (Strategic Innovation Fund (SIF) programme quoted by Citation Flammini, 2018 ). Investments in cost-sharing projects with industry, academia, and government aim to bring technology closer to commercialization and empower sectors to adopt leading technology ( Citation Canada, 2019a ). Dot Technology Corp.™ was one of the 55 projects chosen as lead applicants in the agriculture sector although it is not listed amongst the 15 project candidates advancing to the full application phase of the competition ( Citation Canada, 2019b ). A recent addition to SIF is a new programme stream, National Ecosystems, specifically geared toward supporting innovation by Canadian SMEs ( Citation Canada, 2019c ). This policy is in early stages of implementation and additional information is not currently available. At the provincial level, the aftershock of the Saskatchewan innovation fund programme, SAIF, is evidence of a movement towards government supporting industry-academic collaboration and an outcome of advancing smart farming innovations that support ease-of-use of the new technologies.

The last two policy areas are aftershocks in the sense of policy gaps identified in this study. Canada lacks a regulatory framework and regulations for the safe use of autonomous agriculture equipment. In the absence of rules, Dot Technology Corp.™ created a trailer platform to transport DOT™ and engaged in conversations with the Saskatchewan Government Insurance agency and Transport Canada to establish guidelines for autonomous farm vehicles ( Citation Garvey, 2018 ). CCMTA confirmed a pilot program in Saskatchewan is in the works, and Dot Technology Corp.™ is taking a leadership role (p.comm. Citation Canadian Council of Motor Transport Administrators (CCMTA), 2018 ). The outcome of this activity is thus far not public information.

The final policy aftershock is related to ownership, security and third-party use of agricultural data, and control of the product life cycle of agriculture equipment through the use of IP rights and systems lock-out to equipment repairs traditionally done by the farmer. These are areas of growing global concern and in other jurisdictions, there are pressures for governments to engage with these issues, however, in Canada, there is no obvious action in either area. In the absence of government regulations on farm level information, or Canadian industry codes similar to the ADT principles, the aftershock of firms such as Dot Technology Corp.™ which manufacture equipment that generates data, will be a need to establish one-on-one trust relationships with farmers, provide clarity and transparency on the use of machine data collected by DOT™ and provide assurances on measures the firm has taken to address cybersecurity of their proprietary Cloud systems. The aftershock of using existing provincial statutes which prescribe relationships between equipment industry actors and farmers remains unknown until specific cases are brought forward for review by government oversight boards.

Our case study provides compelling evidence that the inventor of DOT™ has created an IOS where SMEs can rapidly mobilize human and financial resources – they are ‘nimble’ and able to design, manufacture, commercialize, and market innovative and smart agricultural equipment. The innovation addresses a farm level problem relevant to broadacre farming and potentially delivers cost-savings and other less tangible benefits related to reduced physical and mental stress from labour shortages.

9.1 Farming Reimagined: An Unstable IOS

[E]veryone else is working on adapting the tractor technology to be autonomous, where this takes grassroots to look at it and say, why do we need a tractor? (Norbert Beaujot quoted in Citation Rance, 2017 ).

Traditionally, the patent form of IP is a dominant innovation pathway for inventors of agriculture equipment, particularly for entrepreneurs in western Canada. Between 1905 to 1976, about 3200 inventions were patented in Saskatchewan and thousands went unrecorded ( Citation Western Development Museum Patent Index, N.d. ). Beaujot, a holder of numerous equipment patents, is well familiar with the ‘patent pathway’ ( Citation Justia Patents, 2019 ). Industry norms of IP shifted to copyright similar to the information technology (IT) sector, however, as Citation Gordon-Byrne (2014) argues, several elements of software and hardware warranties and technical support in the IT industry are poorly understood. As farmer opposition (e.g. right to repair movement) gained momentum the ‘smart’ technology pathway used in other sectors is less clear for agriculture equipment manufacturers. The DOT™ IOS illustrates a ‘different version’ of a pathway to innovation. The licensing business model with Dot Technology Corp.™ is a fresh approach for the smart farming agriculture equipment industry. In the broader context, the tension over competition for different versions of the future including farmer rights, versus OEMs IP rights, and copyright law, is far from being resolved, even with the new licensing model pathway. Until clarity is brought to these areas, smart farming may remain locked-in to an Unstable type of IOS.

This case further demonstrated that a smart farming IOS requires the design of new regulations, policies, and standards. Analysis of the DOT™ IOS was used to answer each of three research questions and the implications for policy. The first research question to answer is, how are smart farming innovations enabled or limited by public policy, or advancing in the absence of governance models?

9.2 Smart farming IOS: public policy and Canadian governance

This paper presented evidence that Canada has an enabling policy environment supporting smart farming innovation. Federal and provincial policies and associated programmes are available to individual for-profit organizations in the advanced manufacturing sector. Several of the Dot Technology Corp.™ and SeedMaster management felt the federal YEP programme brought good value for them and the graduates and was ‘a feather in the IRAP cap’. The training programme provided a new source of employment for computer programmers and indirectly helped create legitimacy of the innovation. Beaujot and the team assembled to create DOT™ are part of the local agriculture community, trustworthy providers of equipment that meets farmer’s needs and the DOT™ management team has regular contact with farmers and their families, which implicitly makes them accountable for their innovations. This model starkly contrasts the Silicon Valley culture of venture capital prospecting in agriculture by individuals and firms lacking in situational awareness of the problems facing commercial agriculture. They are also far removed from a face to face engagement with farmers and understanding user concerns.

At the provincial level, the Patent Box Programme and the Saskatchewan Advantage Innovation Fund provided financial resources after DOT™ was developed, tested, and recognized as an award-winning innovation, but nonetheless, they have still been useful. According to Beaujot, government’s continued support for entrepreneurs and innovation is key to helping companies like Dot Technology Corp.™ succeed locally. Yet Bronson has framed Canadian government investments available through programs such as those mentioned above as being driven by productivist values. She asserts smart farming innovations promote large-scale capital-intensive farms, benefiting existing powerful players and those who can pay for the technology at the expense of small to medium-sized, “labour-intensive farms” ( Citation Bronson, 2018, 9 ). In some respects, Bronson is correct when one views the policy in isolation, but this case suggests a more nuanced approach for several reasons. First, on the production side, DOT™ is well suited for a farm operation where accessing and retaining labour is a major challenge in comparison to very large farms (6,500 ha or larger) where the staff is employed year-round. Second, DOT™ and DOT Ready™ implements are reasonably priced and affordable for a medium-sized farm operation (1,400 ha), the user interface is familiar technology, relatively easy to operate and with training provided by the developer, the smart farming technology is accessible to a broad range of farmer ages and skills. Third, when the situation is viewed from a manufacturing industry lens, neither Dot Technology Corp.™ nor the sister company, SeedMaster, would be considered a ‘powerful player’. They are SMEs, small in size and distant in location and direct influence on federal government policy-making. Except for the YEP, direct government support for DOT™ was minimal. The management team commented the federal policies were not a barrier to what they achieved, however, they suggest government strategies for innovation reflect a lack of understanding in the technology investment landscape. For example, a better way for SMEs to find and access talent would have accelerated R&D.

From this evidence, one could conclude Canada’s institutional structures are supporting a smart farming future in Canada, yet this study has demonstrated that smart farming is advancing rapidly in the absence of policy and is doing so in three aspects, autonomous vehicle regulation, a policy on agricultural data, and a strategy for communication and information exchange on digital technology-based innovations and evidence of adoption behaviours.

A major challenge advancing the smart farming IOS is related to the shared jurisdiction of policy implementation in Canada. The Constitution Act , 1867, section 95, established agriculture as an area of concurrent jurisdictions, meaning that federal and provincial governments have related (though not identical) jurisdictions ( Citation Atkinson et al., 2013 ; Citation Hedley, 2017 ). The legislative landscape in the Great Rewrite of agriculture warrants attention, particularly regarding coordination of policies outside of the boundary of agriculture departments and at the top of this list is the need for a clear and coordinated policy on autonomous farm vehicles. In January 2018, the Canadian Senate Standing Committee on Transport and Communications reported that government departments “may be working at cross purposes” and advised the creation of a joint policy unit to coordinate federal efforts and implement a nation-wide strategy for autonomous vehicles in Canada ( Citation Senate of Canada, 2018, 11 ). The Committee recommended government agencies should ‘work with’ provincial and territorial governments through the CCMTA to design a ‘model provincial policy’, and put a priority on developing vehicle safety guidelines for the design of autonomous vehicles. Their report did not mention autonomous farm vehicles and until progress is made on the pilot project and insurance scheme and it becomes a starting point for policy discussion, there remains a high level of regulatory uncertainty for new entrants to the market, notably regulations related to liability, and public safety.

This study identified a second major void in the Canadian policy toolkit. Currently, no clear guidelines exist regarding data security, privacy, and transparency when it comes to agriculture data in Canada ( Citation Bronson, 2018 ). Scholars writing in the area of smart farming innovation conclude that public organizations could take a leadership role by creating standards to ensure responsibility for data integration ( Citation Eastwood et al., 2017b ), however, there is no obvious policy action in Canada. Following Wall’s presentation of the FCC survey in November 2018, Citation Booker (2018c) contacted various government agencies, concluding ‘farmers are on their own’ regarding ownership and use of farm data. He found that agricultural data did not fall under the regulatory authority of the Privacy Commissioner of Canada, and that authority of the Competitions Bureau is limited to the Competitions Act and regulating deceptive marketing strategies. Until clarity is brought to the issue of data, the industry is at risk of losing farmer’s trust and potentially hindering innovation opportunities at the farm level.

The third public policy gap is related to research and extension activities. The traditional role of government as the source of farming research information and knowledge transfer to farmers and the public, is being filled by industry actors, not unlike what Citation Rhodes (1997 , Citation 2007) described as ‘hollowing out’ of the role of the state. Agribusinesses and lending institutions play a role in information transfer by offering customized farm management agronomic (production) and marketing support services to their customers. Equipment dealerships and manufacturers of equipment provide brand-specific technical support. However, the dominant actor is without question the farm media, who are well on their way to becoming de-facto innovation brokers, representing a network of ICT actors whom neither create, nor implement, innovations; instead, they enable others (e.g. farmers) to innovate ( Citation Klerkx et al., 2009 ). In a study of farm events (field days, tours, trade shows) in the United States, Heiniger and colleagues found that these venues are a long-standing tradition and more recently, farmer talk (discussion panels) or side-by-side software demonstrations are ranked with high importance by participants ( Citation Heiniger et al., 2002 ). Farmers attending the events “hope to find answers to their problems regarding use of technologies”, and decide if the new tools may be applied to their farm operations (ibid, 310). The farm media, as innovation brokers, will be important actors shaping the smart farming innovation IOS in Canada unless the traditional role of information dissemination, and agriculture knowledge extension is shared or fully resumed by the government.

9.3 Smart farming IOS: a solution to societal concerns

The second research question for this study is, how might smart farming address problems at the farm level, while also reducing the environmental impact of crop production processes ? Canadian policy is a science-based system, however, this study identified a gap in evidence of digital technology use behaviour in the farming population, social and environmental impact, or public perceptions of smart farming. There is a need to re-examine the role of government and data collection (statistics) and information used to enlighten actors on innovation in Canadian agriculture based on new smart farming digital technologies. Ultimately, if gains for society are substantial, but not directly generating returns to farmers, there may need to be more consideration of alternative models of compensation and sharing of benefits and risks of smart farming. Currently, the mandatory national agriculture census is implemented every five years in Canada. In the era of the Great Rewrite, this is sluggish in comparison to the technologies being introduced to markets. In-between these five-year cycles, surveys are done by organizations other than Statistics Canada and while some results are publicly available (e.g. AAFC, academic papers), private firm surveys are generally not publicly available. Citation Bronson (2019) asserts the industry is biased towards serving larger commercial operations, which if true, would tend to skew the evidence to those larger ventures. It is fair to ask ‘what evidence’ is being used to advance smart farming innovation in Canada when industry survey data falls far short of being representative of the population of farm operators, or different farm sizes and types of operations.

The DOT™ IOS case study has also provided evidence not reported elsewhere regarding the need to ‘prove up’ the estimated economic, environmental and social benefits of smart farming. Evidence of the benefits and risks beyond the farm gate is particularly important for smart farming innovations such as the one in this case study, created by an industry SME which excels in manufacturing but lacks the scientific expertise, research skills, and scale of resources to conduct rigorous scientific studies. Actual field experiments will be required to quantify the impact of reduced fuel emissions and soil compaction, as well as the impact of autonomous equipment on the labour shortage problem and the health and well-being of farmers, farm families, and society. There is a role here for the government to directly conduct research demonstrating longer-term environmental and social impacts, or indirectly serve as a coordinating mechanism supporting R&D with multiple institutions, collaborative data collection, and direct farmer participatory (on-farm) research. First steps are being taken to understand the labour and related stress problem. In spring 2019, a pilot program led by Adrian Jones-Bitton, named ‘in The Know’, will offer online mental health resources tailored for Canadian farmers ( Citation University of Guelph, 2018a , Citation 2018b ).

In the absence of industry capacity or government mandate to quantify the economic and environmental impacts of digital technologies in agriculture, decision-makers may draw on policy-lessons learned from an earlier time of major technological change in farming. Governments facilitated coordination among multiple groups of actors who gathered evidence on the problem, demonstrated new technologies and their impact; the outcome was widespread adoption of conservation agriculture technologies ( Citation Sparrow, 1985 ; Citation Acton and Gregorich, 1995 ; Citation Awada et al., 2014 ). Farmer behaviour shifted and the technological changes at the farm level and social acceptance of conservation tillage transformed prairie landscapes from constant tillage and summerfallow ( Citation Carlyle, 1997 ), to a new norm where 87% of total acres of land area on the prairies prepared for seeding are under no-tillage or minimum tillage systems (Statistics Canada, 2016i, Table 32-10-0408-01). Conservation farming technologies and the associated equipment innovations became core to the culture of broadacre farming in the prairie region and the lessons summarized by Citation Lindwall and Sonntag (2010) offer a blueprint on how to nudge farm level behaviours towards acceptance and use of innovations. Similar strategies could be evaluated to support transition towards a new norm of smart farming practices.

9.4 Smart farming IOS: risks of the innovation

The benefits of the DOT™ and DOT Ready™ equipment have been presented in this study, but as an answer to the third research question, three potential risks associated with this IOS were identified. Digitization inherently generates data and easy access to the IoT enables its long-distance transmission. Bottlenecks such as reliable access to the Industrial Internet and sluggish and inconsistent upload and download speeds ( Citation Mark et al., 2016 ) are unquestionably a serious risk for transmission of machine-data and in general, a smart farming future in western Canadian and beyond ( Citation Lyseng, 2017c ; Citation Pant and Hambly Odame, 2017 ). Also, cybersecurity system attacks by malicious actors create an agriculture system-level vulnerability for the operation of ‘connected’ agricultural equipment ( Citation Tzounis et al., 2017 ; Citation Ramachandra et al., 2017 ; Citation Boghossian et al., 2018 ). The scope of the ICT challenge is beyond the capacity of the agri-food/agriculture sectors to resolve alone. New ways of thinking about connectivity are required and this study has demonstrated that finding alternative solutions is possible, but that much more work is needed in this area.

The second area of risk is the new pathway to innovation. New DOT Ready™ equipment is manufactured through a licensing agreement and while there are early indications that this business model is well received by other equipment manufacturers (i.e. the Pattison Liquid Systems SME, and the larger shortliner manufacturing firm, New Leader Manufacturing), Citation Pigford et al. (2018) propose the concept of an innovation ecosystem niche, which has relevance to the DOT™ IOS. An ecosystem niche may be populated by a group who shares a common objective that reflects a culture of shared knowledge and skills. DOT Ready™ equipment made by these two firms represent a bold new vision of the future of farming on the prairies, however, multi-national OEMs, like John Deere™, might determine that the market for autonomous, tractor-less equipment is large enough that they move to ‘acquire’ the innovation, just as they did with Blue River Technologies and other agtech firms ( Citation Cosgrove, 2018 ). Farmers could be left with an autonomous piece of equipment and little control over repairs, warranties or servicing, and their data would be aggregated in the John Deere™ Cloud ( Citation Phillips et al., 2019 ). If, however, a multi-national such as AGCO™ presents a corporate buy-out option to Dot Technology Corp.™., the IOS might evolve to a more open systems strategy for autonomous equipment. Either of these possibilities would fit the regime takeover risk identified by Pigford and colleagues, inevitably re-shaping the ‘Farming Re-imagined’ by inventor, Beaujot.

Absence of government policy or industry standards regarding ownership and control of agricultural data is creating a third area of risk and in-action may be creating a farmer ‘trust-risk’ dilemma. This policy void is extremely problematic for longer-term smart farming innovation and the sharing of farm-level data for future benefit. For example, big data analytics could be used for benchmarking machine/model performance, informing and assessing global and regional trends and sector innovation such as the conservation tillage practices, or supporting R&D for global breeding programmes and other smaller players ( Citation Huang et al., 2018 ; Citation Janzen, 2018b , Citation 2019c ; Citation Waltz, 2017 ).

9.5 Limitations

As with any case study approach to research, there are limitations to the validity of the conclusions drawn from the data used to inform the case ( Citation Creswell, 2015 ). In this study, multiple data sources are used when available and the participants as experts in agriculture and smart farming innovation bring “context-dependent knowledge and experience” which address threats to the internal validity of the case study ( Citation Flyvbjerg, 2006, 222 ). But there are constraints related to the narrow characteristics of the population sampled, thereby restricting general claims made about the IOS, which itself is not without its limitations, and particularly, an Unstable IOS. For example, having two firms manufacturing DOT Ready™ implements, other than Dot Technology Corp.™’s sister company, SeedMaster, is not enough to demonstrate a clear pathway or need of major policy changes for autonomous farm vehicles. Furthermore, for the Aftershock element of the IOS in this case study, the data simply does not yet exist. With this in mind, data captured in this area is therefore speculative in nature, focusing on the ‘putative aftershock’ including socio-economic benefits accruable to society. Future studies would benefit from having empirical data on farmer views and other equipment manufacturer’s perspectives, and their responses to the autonomous smart farming technology offered in DOT Ready™ equipment, particularly as the multiple pieces of equipment have been used by early adopter farmers in 2018 and 2019 and are made locally.

The purpose of this study was to identify opportunities as well as challenges associated with innovations in smart farming technologies, particularly as they pertain to agricultural equipment for broadacre farming systems. Innovations in smart farming technologies can, and should be, framed as a solution to farmer’s problems if the goals for increasing food supply through the use of new digital technologies are to be realized. At the farm level, the DOT™ IOS is suggestive of dollars and cents opportunities to reduce farm input economic costs (equipment, fuel, labour), and data is being collected on input cost savings, for example crop inputs. Other potential benefits are far more difficult to quantify, including improved farmer health, welfare, and safety, and improved soil health.

The SME featured in the case study demonstrates that there are opportunities to address equipment-related interoperability constraints at the farm level. Through a licensing model such as that offered by Dot Technology Corp.™, any equipment manufacturer can convert their proprietary equipment technology to function harmoniously with the autonomous platform, thus making the autonomous technology ‘systems-level interoperable’ (i.e. across any type of equipment brand or firm size). It is also possible that farmers can be empowered to make basic equipment repairs without jeopardizing warranties, for example, replace a sensor. While most farmers likely would not want to do the highly technical digital technologies-based repairs on their equipment, basic ‘plug-and-play’ replacement of components could be included as part of a farmer’s basic toolkit. When a malfunction is identified via error codes, the sensor is replaced with assistance from a remote technical support team. Furthermore, smart farming technologies need not be complex. An easy to use, familiar and industry-standard interface, in combination with training sessions can help farmers along the learning curve of technology adoption, shifting the frame from digital technologies as ‘too complex’ and a barrier to adoption, to an opportunity to develop new digital technology based skill sets supporting farming operations.

The fundamental challenge for a smart farming future is basic to the role of government, industry, and society in general. This study has demonstrated that the ‘stage is set’ for investment and smart farming technologies are rapidly being commercialized, with, or without an enabling policy environment. There is a need to take action now and mobilize resources to address the challenges on the horizon for smart farming, yet who pays for shaping the Great Rewrite future of agriculture? Supporting farmer adoption and societal acceptance of a smart farming future will require investment and evidence of a problem if limited government resources are to be appropriated. The Organization for Economic Co-operation and Development ( Citation OECD, 2018 ) reports support to farmers in the OECD as a share of gross farm receipts is trending downward, decreasing from 36.9% to 18.2% from 1986-88 to 0.7% in 2015-17. On the other hand, food is viewed as a basic need, and recognizing that the agriculture sector has been ‘drastically under-funded’, venture capitalists view the sector as ‘wide open’ for investment ( Citation Waltz, 2017 ). The scale and the increasing pace of investment is substantial. AgFunder tracks venture capital funding and there was a 43% year-over-year increase in agtech investment between 2012 and December 2018, with a six-year total of US$ 55.5 B ( Citation AgFunder 2017 , 2018, 2019). As observed by the industry, it is a ‘digital wild west’ ( Citation Tatge, 2016 ).

A coordinated approach could bring stability to the present Unstable IOS for smart farming innovation such as this case study has presented. Three potential scenarios, in particular, may warrant further analysis. In the first scenario, “specific, concrete instruments” could be developed such as industry-wide codes and standards ( Citation Asveld et al., 2015, 584 ). Standards could include principles for repair of essential agriculture equipment used in time sensitive periods of seeding and harvest operations, or universal codes for agricultural data rather than the current voluntary system or absence of policy instruments prescribing governance of farm level data. In the second scenario, public-private partnerships as described by Citation Hermans et al. (2018) and Citation Eastwood et al. (2017b) could be used as a means to engage stakeholder groups and set objectives for the design and implementation of programs to ‘prove up’ the impact of digital technology-based innovations and disseminate information to farmers and the general public. Another area where partnerships may be worthy of evaluating is access and availability of IoT, and enhanced security of ICT systems. Interim solutions to improve Internet connectivity in rural communities have been developed but these are viewed as a band-aid solution to a bigger problem ( Citation Lyseng, 2017c ). The scale of the system needed to provide coverage in rural areas is beyond the scope of the agri-food sector or government to resolve alone. New technologies or different approaches to public-private sector partnerships are needed to support connectivity infrastructure and ICT requirements for smart farming. A third scenario, offered by Citation Bronson (2018) , encourages participation in smart farming innovation systems by end-users representing a broad spectrum of farm sizes and production types, the private sector, and citizens. This systems-level engagement process could help understand potential social and ethical risks associated with smart farming, a concern noted by Citation Régan et al. (2018) . An outcome of this scenario could be a foundation, or another institutional structure with a vision for a responsible innovation systems approach to a smart farming future ( Citation Long and Blok, 2018 ; Citation Rose and Chilvers, 2018 ). These scenarios may enable a reflexive, systems-level approach to a smart farming future and foster a culture of farm to fork engagement in sustainable agriculture and food production in the twenty-first century using smart farming technologies.

As a final thought, the smart farming future for Canada should not be primarily focused on the ‘new thing’ in digital technologies or the technological changes, for as this research has shown, smart farming innovations designed and manufactured by SMEs will be created in presence or absence of innovation, economic development incentives or other government policy. However, the absence of government policy for governance of agricultural data is creating a trust-risk for a smart farming IOS and this potentially frames the rapidly unfolding Great Rewrite and machine data aspects of smart farming, as an un-structured policy problem for policy makers and one in ‘need of criteria to identify a solution’ ( Citation Simon, 1973 ) as new information unfolding in the Great Rewrite of agriculture is assimilated.

The authors declare no conflicts of interests in the companies and industries examined in this paper. This paper is based on a policy study funded by the government of Canada, Strategic Policy Branch Contract 01B68, and the Social Science and Humanities Research Council Creating Digital Opportunities Partnership Grant (project number 416303).

Acknowledgement

The authors owe a debt of gratitude to the management team at Dot Technology Corp.™ and Pattison Liquid Systems for their time and willingness to share their experiences. The authors thank the reviewers for their suggestions for revisions. Their comments were a valuable opportunity to improve the publication. Our sincere thanks are also extended to those who have helped in numerous ways: Jeremy Rayner, Bill Boland, Graeme Jobe, Debra Hauer, Krista Maclean, Fred Wall and team, Blair Hudyma, Delaney Seiferling, and Barbara Douglas.

Related Research Data

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Methodology: Three Stages

The first methods stage focused on understanding the types of digital technology and scale of applications in commercial agriculture in western Canada. Events were chosen to answer the CDO project research question, how does the diffusion of digital technology across all sectors of the economy contribute to the overall dynamism and competitiveness of the Canadian economy ?

A strategy of ‘purposeful sampling’ was used to “purposefully inform an understanding of a research problem and central phenomenon in the study” ( Citation Creswell, 2015, 156 ). Primary and secondary data was gathered from sites where farmers (consumers of the technology) and agribusiness firms (suppliers and/or developers of the technology) gathered to observe, discuss and potentially purchase some form of digital technology application for farming. Trade shows and industry events were selected as representative venues which “bring together different groups of suppliers from a particular industry or technology field with the primary goal to showcase, promote, and/or market their products and services to buyers and other relevant target groups” ( Citation Bathelt et al., 2014, 4 ).

A total of fourteen venues were attended from 2015 to 2017. The events, attendance and number of exhibitors is listed in Table A1 .

Primary (observational) data was collected on the innovation and on which exhibits/exhibitors were attracting the most farmer attention. These database entries were later cross-referenced to exhibits/exhibitors receiving peer recognition in the form of People’s Choice and panel-judged innovation awards. Information on acquisitions, mergers and new entrants was added to the database to reflect changes in the type and number of innovations being offered over the three-year time period. Secondary data included event brochures with information on exhibits. In addition, media coverage in the form of newspaper circulars and articles was collected to understand how innovations were reported to the farming community.

The second stage involved ‘opportunistic sampling’ which relied on “taking advantage of unforeseen opportunities” at each event ( Citation Ritchie et al., 2012 , 81). Criterion for sampling was incorporation of some form of digital technology for use in the agriculture sector, willingness of exhibitors to participate in the research and the innovation being either nominated or a direct recipient of an innovation award. The number of possible research participants ranged from 25 to 50 at each venue. There were also different levels of accessibility as not all exhibits were staffed by someone who could explain the genesis and development of each innovation. However, a broad diversity of types of technologies was available. Following communication with exhibitors, relationships were established, contact information was exchanged and individuals were formally invited to participate in the research project. Information was then provided (email or paper copy) to participants on the project’s goals, funding source, time required for interviews and the ethics statement.

Table A2 Interview guide 1: Creating Digital Opportunities project.

Table a3 technologies and number represented in the interviews, june 15 to november 2017..

Participants represented a range of firm sizes, from one or two employees to several hundred employees, with operations headquartered in the prairies or the northern United States. Several firms had a customer distributed across North America, Australia, and the Slavic regions (dryland agriculture farming conditions). All participants were asked to explain the challenges (barriers) they experienced and the barriers and opportunities they envision for digital technologies in Canadian agriculture. They were also asked to identify policy areas or gaps that either supported or hindered the advancement of their innovations, or are on their radar as emerging areas of concerns related to digital technology-related innovations in agriculture and knowledge-based systems.

Before stage 3 interviews were undertaken, a literature review of material related to smart farming and autonomous technologies was conducted, including secondary data from agriculture industry reports, blogs, and tech news magazines. An interview guide was then designed as a specific series of theoretically-informed interview questions ( Table A4 ).

Third stage ‘purposive sampling’, was initiated at a July 2017 outdoor farm event where DOT™ was unveiled and demonstrated to the public. Senior management of SME, SeedMaster (owned by the inventor of DOT™) had been interviewed in an earlier phase of data collection for the CDO project. Based on this prior research relationship, an informal meeting was granted at the SeedMaster exhibitor display following the field demonstration of DOT™. Further arrangements were made for a series of interviews with the inventor and five members of the senior management team representing SeedMaster and the newly formed sister company, Dot Technology Corp.™

Table A5 Interview guide 2: Smart Farming project.

Secondary data is sourced from various documents including farm media reports and press announcements on awards and government funding decisions. These were imported into NVivo for document analysis and

Table A6 Main and sub-themes coded from interview transcripts and secondary data sources, 2018.

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Smart Farming Technology Trends: Economic and Environmental Effects, Labor Impact, and Adoption Readiness

• Agronomy 10(5):743

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• Agricultural University of Athens

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The future of farming: ai innovations that are transforming agriculture.

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AI-assisted Agriculture

Agriculture is a cornerstone of human civilization, a testament to our ability to harness nature for sustenance. Yet, this age-old industry faces many challenges that hamper productivity, impact livelihoods, and threaten global food security.

By 2050, we must produce 60 percent more food to feed a world population of 9.3 billion, reports the Food and Agriculture Organization. Given the current industry challenges, doing that with a farming-as-usual approach could be tricky. Moreover, this would extend the heavy toll we already place on our natural resources.

This is where Artificial Intelligence can come to our rescue. The AI in Agriculture Market is projected to grow from $1.7 billion in 2023 to $4.7 billion by 2028, highlighting the pivotal role of advanced technologies in this sector. This article explores three significant issues agriculture faces today and shows how AI is helping tackle them using real-world examples.

Three key challenges farmers face

Amongst the many issues hurting farmers, three stand out due to their global presence and financial impact:

1. Pests : Pests devour approximately 40% of global agricultural productivity annually, costing at least $70 billion. From locust swarms decimating fields in Africa to fruit flies affecting orchards, the impact is global, and financial repercussions are colossal.

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2. Soil Quality and Irrigation : Soil degradation affects nearly 33% of the Earth's soil, diminishing its ability to grow crops, leading to a loss of about $400 billion. Water scarcity and inefficient irrigation further dent agricultural output. Agriculture uses 70% of the world's accessible freshwater, but 60% of it is wasted due to leaky irrigation systems.

3. Weeds : Despite advancements in agricultural practices, weeds cause significant declines in crop yield and quality. Around 1800 weed species reduce plant production by about 31.5%, leading to economic losses of about $32 billion annually.

How AI is transforming Agriculture

Smart Farming

Artificial Intelligence is often used as a catchall phrase. Here, it refers to the systematic collection of data, pertinent use of analytics ranging from simple descriptive summaries to deep learning algorithms, and advanced technologies such as computer vision, the internet of things, and geospatial analytics. Let’s look at how AI helps address each of the above challenges:

1. Pest identification and control : Accurate, early identification and control of pests is essential to minimize crop damage and reduce the reliance on chemical pesticides. Data such as weather reports, historical pest activity, and high-resolution images captured by drones or satellites are readily available today. Machine learning models and computer vision can help predict pest invasions and identify pests in the field.

For example, Trapview has built a device that traps pests and identifies them. It uses pheromones to attract pests, which are photographed by a camera in the device. By leveraging Trapview’s database, AI identifies over 60 pest species, such as the codling moth, which afflicts apples, and the cotton bollworm, which can damage lettuce and tomatoes.

Once identified, the system uses location and weather data to map out the likely impact of the insects and pushes the findings as an app notification to farmers. These AI-driven insights enable timely and targeted interventions, significantly reducing crop losses and chemical usage. Trapview reports that its customers have seen a 5% increase in yield and quality, and overall savings of 118 million euro in growers’ costs.

2. Soil health monitoring : Continuous monitoring and analysis of soil health are essential to ensuring optimal growing conditions and sustainable farming practices. Optimizing water use is crucial to ensuring crops receive precisely what they need, reducing waste and enhancing productivity.

Data from in-ground sensors, farm machinery, drones, and satellites are used to analyze soil conditions, including moisture content, nutrient levels, and the presence of pathogens. Such soil health analysis helps predict water needs and automate irrigation systems.

For example, CropX has built a platform specializing in soil health monitoring by leveraging real-time data to help users review and compare vital parameters alongside crop performance. Farmers gain insights into soil type and vegetation indices like NDVI - normalized difference vegetation index, SAVI - soil adjusted vegetation index, and soil moisture index to optimize crop management strategies. CropX reports that its solutions have led to a 57% reduction in water usage, a 15% reduction in fertilizer usage, and up to 70% yield increase.

3. Weed Detection and Management : Precise identification and elimination of weeds is critical to preventing them from competing for precious resources with crops and minimizing herbicide use. Thanks to computer vision, drones and robots can now identify weeds amongst crops with high precision. This allows for targeted weed control, either mechanically or through precise herbicide application.

For instance, the startup Carbon Robotics leverages deep learning algorithms in its computer vision solution. It identifies weeds by analyzing data from over 42 high-resolution cameras that scan the fields in real-time. Then, it employs robotics and lasers to deliver high-precision weed control.

The LaserWeeder claims to weed up to two acres per hour and eliminate up to 5,000 weeds per minute at 99% accuracy. Its growers report reducing weed control costs by up to 80% with a potential return on investment in one to three years.

Tackling the risks of automation

Opportunities and risks of AI in agriculture

AI has numerous benefits for agriculture but isn’t without inherent risks , such as job displacement, ownership concentration, and ethical concerns. When AI automates tasks traditionally done by humans in large numbers, it could lead to job losses across both manual and cognitive roles. Moreover, it could exacerbate ownership concentration, benefiting large enterprises or wealthy individuals at the expense of smaller farms.

When farmland turns into a hotbed for data collection – underground, at the crop level, and from the sky, this could lead to data privacy issues. These challenges underscore the need for careful consideration and governance to balance AI's advantages against its potential downsides. This is unique not just to the agricultural sector but to all industries where AI is being applied.

Ushering in a transformative future

Integrating AI in agriculture is not just reshaping current practices but also paving the way for a sustainable and resilient future. AI could become a master gardener, perpetually monitoring and fine-tuning every growth stage in the farm, from seed selection to harvest and beyond. It can help adjust farming practices in real time to climatic shifts, ensuring optimal crop health and yield.

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Agriculture

Make Your Note

Adoption of Modern Technology in Agriculture

• 04 Apr 2022
• GS Paper - 3
• Government Policies & Interventions
• E-Technology in the Aid of Farmers

For Prelims : IDEA, genetic engineering, artificial intelligence, block chain, remote sensing, GIS technology, use of drones, SMAM, Kisan Call Centres, Kisan Suvidha App, Agri Market App.

For Mains: E-Technology in the Aid of Farmers

Why in News?

Recently, the Union Minister of Agriculture and Farmers Welfare in a written reply in Rajya Sabha informed about the various initiatives taken by the government for adopting technology in Agriculture.

• In 2021, a consultation paper on the India Digital Ecosystem of Agriculture (IDEA) from the Ministry of Agriculture and Farmers’ Welfare (MoA&FW) was released, which talks about a digital revolution in the agriculture sector.
• The adoption of modern technology depends on various factors such as socioeconomic conditions, geographical conditions, crop grown, irrigation facilities etc.

What is the importance of Technology in Agriculture?

• Technology in agriculture can be used in different aspects of agriculture such as the application of herbicide, pesticide, fertilizer, and improved seed.
• Presently, farmers are able to grow crops in areas where they were thought could not grow, but this is only possible through agricultural biotechnology.
• Such engineering boosts the resistance of the crops to pests (e.g. Bt Cotton) and droughts . Through technology, farmers are in a position to electrify every process for efficiency and improved production.

How using Technology can be Beneficial in Agriculture?

• Increases agriculture productivity.
• Prevents soil degradation.
• Reduces chemical application in crop production.
• Efficient use of water resources.
• Disseminates modern farm practices to improve the quality, quantity and reduced cost of production.
• Changes the socio-economic status of farmers.

What are the Related Challenges?

• Lack of knowledge
• Inadequate skills
• Lack of improved skills
• Poor infrastructure
• Lack of storage
• Lack of transport
• Lack of Money
• Access to credit
• Lack of access to Bank Loans
• Poor soils,
• Soil fertility
• Unreliable rainfall
• Natural disasters such as floods, frost, hail storm
• Workers have no interest in agriculture, Farm works are not preferred over ipelegeng projects (self-reliance works), Farm jobs are time consuming.

What are the Steps taken by the Government in the Direction?

• AgriStack: The Ministry of Agriculture and Farmers Welfare has planned creating ‘AgriStack’ - a collection of technology-based interventions in agriculture.
• Digital Agriculture Mission: This has been initiated for 2021 -2025 by the government for projects based on new technologies like artificial intelligence , block chain , remote sensing and GIS technology , use of drones and robots etc.
• Unified Farmer Service Platform (UFSP): UFSP is a combination of Core Infrastructure, Data, Applications and Tools that enable seamless interoperability of various public and private IT systems in the agriculture ecosystem across the country.
• In 2014-15, the scheme was further extended for all the remaining States and 2 UTs.
• Under this Scheme, subsidies are provided for purchase of various types of agricultural equipment and machinery.
• Other Digital Initiatives: Kisan Call Centres , Kisan Suvidha App , Agri Market App , Soil Health Card (SHC) Portal, etc.

Way Forward

• The use of technology has defined the 21 st century. As the world moves toward quantum computing, AI, big data, and other new technologies, India has a tremendous opportunity to reap the advantage of being an IT giant and revolutionize the farming sector. While the green revolution led to an increase in agricultural production, the IT revolution in Indian farming must be the next big step.
• There need to be immense efforts to improve the capacities of the farmers in India – at least until the educated young farmers replace the existing under-educated small and medium farmers.
• Technology in agriculture has the potential to truly lead India to be “Atmanirbhar Bharat” in all respects, and be less dependent on extraneous factors.

UPSC Civil Services Examination, Previous Year Questions (PYQs)

Q. Consider the following statements: (2017)

The nation-wide ‘Soil Health Card Scheme’ aims at

• expanding the cultivable area under irrigation.
• enabling the banks to assess the quantum of loans to be granted to farmers on the basis of soil quality.
• checking the overuse of fertilizers in farmlands.

Which of the above statements is/are correct?

(a) 1 and 2 only (b) 3 only (c) 2 and 3 only (d) 1, 2 and 3

• Soil Health Card (SHC) is a GoI scheme promoted by the Department of Agriculture and Co-operation under the Ministry of Agriculture and Farmers’ Welfare. It is being implemented through the Department of Agriculture of all the State and Union Territory Governments.
• A SHC is meant to give each farmer, soil nutrient status of the holding and advise on the dosage of fertilizers and also the needed soil amendments, that should be applied to maintain soil health in the long run.
• The main aim behind the scheme is to find out the type of a particular soil and then provide ways in which farmers can improve it.

Source: PIB

Essay on Agriculture for Students and Children

500+ words essay on agriculture.

Agriculture is one of the major sectors of the Indian economy. It is present in the country for thousands of years. Over the years it has developed and the use of new technologies and equipment replaced almost all the traditional methods of farming. Besides, in India, there are still some small farmers that use the old traditional methods of agriculture because they lack the resources to use modern methods. Furthermore, this is the only sector that contributed to the growth of not only itself but also of the other sector of the country.

Growth and Development of the Agriculture Sector

India largely depends on the agriculture sector. Besides, agriculture is not just a mean of livelihood but a way of living life in India. Moreover, the government is continuously making efforts to develop this sector as the whole nation depends on it for food.

For thousands of years, we are practicing agriculture but still, it remained underdeveloped for a long time. Moreover, after independence, we use to import food grains from other countries to fulfill our demand. But, after the green revolution, we become self-sufficient and started exporting our surplus to other countries.

Besides, these earlier we use to depend completely on monsoon for the cultivation of food grains but now we have constructed dams, canals, tube-wells, and pump-sets. Also, we now have a better variety of fertilizers, pesticides, and seeds, which help us to grow more food in comparison to what we produce during old times.

With the advancement of technology, advanced equipment, better irrigation facility and the specialized knowledge of agriculture started improving.

Furthermore, our agriculture sector has grown stronger than many countries and we are the largest exporter of many food grains.

Get the huge list of more than 500 Essay Topics and Ideas

Significance of Agriculture

It is not wrong to say that the food we eat is the gift of agriculture activities and Indian farmers who work their sweat to provide us this food.

In addition, the agricultural sector is one of the major contributors to Gross Domestic Product (GDP) and national income of the country.

Also, it requires a large labor force and employees around 80% of the total employed people. The agriculture sector not only employees directly but also indirectly.

Moreover, agriculture forms around 70% of our total exports. The main export items are tea, cotton, textiles, tobacco, sugar, jute products, spices, rice, and many other items.

Negative Impacts of Agriculture

Although agriculture is very beneficial for the economy and the people there are some negative impacts too. These impacts are harmful to both environments as the people involved in this sector.

Deforestation is the first negative impact of agriculture as many forests have been cut downed to turn them into agricultural land. Also, the use of river water for irrigation causes many small rivers and ponds to dry off which disturb the natural habitat.

Moreover, most of the chemical fertilizers and pesticides contaminate the land as well as water bodies nearby. Ultimately it leads to topsoil depletion and contamination of groundwater.

In conclusion, Agriculture has given so much to society. But it has its own pros and cons that we can’t overlook. Furthermore, the government is doing his every bit to help in the growth and development of agriculture; still, it needs to do something for the negative impacts of agriculture. To save the environment and the people involved in it.

FAQs about Essay on Agriculture

Q.1 Name the four types of agriculture? A.1 The four types of agriculture are nomadic herding, shifting cultivation, commercial plantation, and intensive subsistence farming.

Q.2 What are the components of the agriculture revolution? A.2 The agriculture revolution has five components namely, machinery, land under cultivation, fertilizers, and pesticides, irrigation, and high-yielding variety of seeds.

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JSmol Viewer

Research progress on autonomous operation technology for agricultural equipment in large fields.

1. Introduction

2. onboard environmental sensing technology, 3. complete-coverage path-planning technology, 3.1. classical path-planning algorithm, 3.2. bionics-based path-planning algorithms, 4. autonomous operation control technology, 5. conclusions and prospection, 5.1. conclusions, 5.2. prospection, author contributions, data availability statement, conflicts of interest.

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Click here to enlarge figure

Method	Sensor Type	Characteristics	Sensing Task
Vision Sensors	Monocular Camera	Monocular cameras are low cost and provide rich image information, but lack depth data and are susceptible to environmental influences.	Farmland boundary detection, navigation line extraction
	Binocular Camera	Binocular cameras can provide rich image information and highly reliable depth information, but the configuration and calibration are more complicated; the computation is large, and parallax calculation depends on computing resources.	Farmland boundary detection, navigation line extraction
	RGB-D Camera	RGB-D camera can provide an RGB map and a depth map, and the calculation amount is small. However, the measurement range is narrow, the noise level is high, the field of view is small, and it is easily interfered with by daylight.	Farmland boundary detection
Radar Sensor	Lidar	LIDAR is highly accurate, stable, and reliable. However, it has a high cost, is susceptible to dust interference with the limited detection range, and cannot recognize color and texture in farmland boundary identification and navigation line extraction.	Navigation line extraction

Camera Type	Features	Advantages	Common Cameras
RGB Cameras	A standard color camera that captures images in the red, green, and blue color channels.	RGB cameras provide rich color and texture information that helps distinguish between different types of obstacles, are low cost, and are easy to integrate and deploy.	Logitech C920, Sony Alpha Series (Logitech, Lausanne, Switzerland)
Depth Camera	In addition to capturing RGB images, it also acquires depth information for each pixel.	Combining depth information and RGB images improves the accuracy and reliability of obstacle detection, providing more precise obstacle localization, especially in complex environments.	Intel RealSense (Intel, Santa Clara, CA, USA), Microsoft Kinect 360 (Microsoft 360, Washington, DC, USA)
Stereo Camera	Captures stereo images through two cameras and uses parallax to calculate depth information.	Provides high-precision depth perception for fine obstacle detection tasks and is more reliable than a single depth camera in terms of detection accuracy and range.	ZED Series (ZED Series, San Francisco, America), Bumblebee2 (Teledyne FLIR, Washington, DC, USA)
Panoramic Camera	Capable of capturing images or videos with a 360-degree field of view.	In obstacle detection, it provides a comprehensive view of the environment, reduces blind spots, and improves the coverage and accuracy of obstacle detection.	Ricoh Theta (RICOH, Tōkyō, Japan), Insta360 Pro (insta360, Shenzhen, China)

Classification	Common Algorithms	Common Application Areas
Algorithms based on graph search	Dijkstra, A *, D *	Global path planning
Algorithm based on sampling	RRT	Global path planning
Algorithms based on artificial potential fields	Artificial potential field method	Local path planning
Algorithms based on curve fitting	Arcs and straight lines, polynomial curves, spline curves, Bessel curves, differential flatness	Local path planning
Algorithms based on numerical optimization	Describing and solving planning problems using objective functions and constraints	Local path planning
Intelligent algorithms based on bionics	Genetic algorithms, particle swarm optimization algorithms, ant colony algorithms	Global path planning, local path planning

Step	GA	PSO	ACO
Initialization	Initialize population	Initialize particles	Initialize ants
Fitness Eval.	Evaluate fitness	Evaluate fitness	Evaluate fitness
Selection	Roulette wheel selection	N/A	Select next node based on probability
Crossover	Single-point crossover	N/A	N/A
Mutation	Swap mutation	N/A	N/A
Update Ind.	Replace individual	Update velocity and position	Update pheromone
Update Best	Find best individual	Update global best	Find global best path
Iteration Loop	Repeat for max generations	Repeat for max iterations	Repeat for max iterations
Return Result	Return best individual	Return global best	Return global best path

Algorithm Category	Global Search Ability	Convergence Speed	Computational Complexity	Adaptability	Scalability
Genetic algorithm	★★★★	★★	★★★★	★★★★	★★★
Particle swarm optimization	★★★★	★★★★★	★★★	★★★	★★★★
Ant colony algorithm	★★★★★	★★★	★★★★	★★★★	★★★★

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Wei, W.; Xiao, M.; Duan, W.; Wang, H.; Zhu, Y.; Zhai, C.; Geng, G. Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields. Agriculture 2024 , 14 , 1473. https://doi.org/10.3390/agriculture14091473

Wei W, Xiao M, Duan W, Wang H, Zhu Y, Zhai C, Geng G. Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields. Agriculture . 2024; 14(9):1473. https://doi.org/10.3390/agriculture14091473

Wei, Wenbo, Maohua Xiao, Weiwei Duan, Hui Wang, Yejun Zhu, Cheng Zhai, and Guosheng Geng. 2024. "Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields" Agriculture 14, no. 9: 1473. https://doi.org/10.3390/agriculture14091473

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Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming

• Open access
• Published: 09 September 2024
• Volume 4 , article number  12 , ( 2024 )

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• Swetha Makam 1 ,
• Bharath Kumar Komatineni 1 ,
• Sanwal Singh Meena 1 &
• Urmila Meena 2  

The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs.

Graphical Abstract

The recent literature from 2014 to 2024 on UAVs in agriculture were examined by PRISMA Methodology

The overview of IOT system (sensors, cameras, micro-controllers and connectivity gateways) in UAVs were provided.

The applications of UAVs in smart farming were compared and summarized by emphatical data and case studies.

The limitations and future views of UAVs in farming are provided.

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• Artificial Intelligence

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1 Introduction

Agricultural automation is a rapidly evolving topic due to increasing population, there is need for requirement of food. The population in India inclined about 0.81% from year 2022 and reached 1.42 billion for year 2023 whereas China population declined 0.15% from year 2022 and reached 1.41 billion in year 2023 [ 1 , 2 ]. India’s food production in 2022–2023 was a recorded as 329.7 MT, whereas China was recorded 686.53 MT which is half of China’s production [ 3 , 4 ]. As a result, in order to feed the world’s fastest-growing population, food production will have to rise by over 50%. The quantity of food produced through conventional methods isn’t adequate to meet demand. Therefore, the goal is to increase production efficiency by optimizing the use of resources such as land, water, and labor. As a result, there is an urgent requirement for progressive agricultural development. Multiple climate factors like humidity, land availability and annual rainfall can’t be controlled. The parameters like application of fertilizers and pesticides, seed rate, labor requirement, water requirement, timeliness of operation, operational losses, drudgery and occupational health hazards can be controlled and monitored by employing modern technologies like as UAVs and IoT. To minimize agricultural inputs and maximize output, transitioning to modern agricultural methods is essential. This change directly benefits farmers and helps reduce hunger. The current era in modern agriculture is dominated by agricultural drones.

In the fourth era, known as “Agriculture 4.0,” traditional farming practices are being integrated with Information and communication technology (ICT) [ 5 ]. This transformation is driven by smart farming techniques, including the Internet of Things (IoT), unmanned aerial vehicles (UAVs), remote sensing (RS), and machine learning (ML) [ 6 ]. The transformation of agriculture from 1.0 to 4.0 is shown in Fig.  1 . The use of UAVs is becoming more prevalent internationally, providing innovative smart guidance and device information to boost productivity [ 7 , 8 ].

Transition of agriculture (1.0 to 4.0) [ 9 ]

Enabling real-time data or information sharing across autonomous networks was the main goal [ 10 , 11 ]. Real-time data-containing smart computational sensors can connect with anyone, anywhere, and at any time over a wireless network [ 12 ]. Wireless sensors and radio frequency identification via WIFI, Bluetooth, and GSM are some of the communication methods accessible on the market that enable connectivity between devices and networks [ 13 , 14 ].

1.1 Visualizing scientific landscapes (VOS) viewer keywords co-occurrence map

The first step involves extracting 739 articles using the two keywords (Agriculture and unmanned aerial vehicles) from the PubMed database in TXT Format. Additionally, a visualization of the keywords’ co-occurrence and the reference’s co-citation has been provided. Some criteria have been chosen for the map’s creation, such as a threshold that requires the terms to appear at least (3 times). Out of 3851 keywords, only 1132 keywords satisfy the chosen threshold requirements. Consequently, Fig.  2 illustrates the overlay visualization of co-occurrence of keywords.

Keywords co-occurrence overlay visualization

The analysis of publications was based on a search for documents using specific terms that were entered into the Scopus and google scholar databases. The results of the initial Scopus and Google searches were analyzed to separate with duplicate content, reclassify some articles using the approach through to be most appropriate given their content and perhaps most importantly to omit articles that did not fall under the purview of this article. Figure  3 depicts the study’s approach of the articles.

Flowchart showing articles identification, screening and included for the study

2 Materials and methodology

Unmanned Aerial Vehicles (UAVs), originally invented for military applications, are popularly known as drones today. In the world of technology, UAVs are also referred to as dynamically remotely operated navigation equipment (DRONE). Drones are airborne systems or aircrafts which are operated remotely by a human. They are equipped with rotors (propellers) that allow them to hover, take off, and fly. The basic principle involves using the rotors to generate thrust and lift, which propels the drone into the sky. Equipped with built-in sensors and the aid of a GPS, these flying robots can be remotely instructed or fly autonomously employing software-driven flight plans through their embedded systems [ 15 ].

2.1 Types of drones

Drones have lightweight frames which are equipped with different technologies. Drones vary in size, weight, payload capacity, flight time and functionality. This is dependent on the purpose for which they are deployed. The flight time of a drone is significantly impacted by its weight and the battery attached to it. There are primarily two types of drones: fixed-wing drones and Rotary drones. Rotary drones can be both single-rotor drones and multi (tri, quad, hexa, octo) rotor drones [ 16 ]. According to the Drone Rules 2021 released by the Ministry of Civil Aviation, Government of India, drones are now classified based upon the maximum all-up weight, including payload, nano drone—less than or equal to 250 g.

Micro drone—greater than 250 g and less than or equal to 2 Kilograms (kg)

Small drone—greater than 2 kg and less than or equal to 25 kg

Medium drone—greater than 25 kg and less than or equal to 150 kg

Large drone—greater than 150 kg

Among them Fixed wing and Single rotor small drones are utilized in agriculture sector. Multi- rotor micro drones are utilized in photography. Whereas, Medium and large categorized drones are utilized for military purposes [ 15 ]. The various types of drones are given in Table  1 .

Application of types of Drones in various agricultural operations are shown in Fig.  4 and Table  2 . There are many other agricultural applications in which usage of drones are negligible are tillage, irrigation, intercultivation (weed infestation) and threshing. The following activates.

Leading applications of UAVs in smart farming operations in percentage [ 17 ]

2.2 Basic components of a UAVs

Basic components of agricultural drones include controller systems (micro-controllers/flight controllers) propulsion systems (BLDC Motors, Impellers), IOT System incudes sensors, camera, navigation, power systems (batteries) and other payload components (sprayers, sprinklers, seed dispensers and cutting blade’s). The power and work flow in components of UAVs are shown in Fig.  5 a, b. The battery is the power source which provided power to the sensors, micro-controllers and motor drivers. The sensors are the components which senses and collects the data of parameters like direction, orientation, wind velocity etc., are shown in Fig.  7 and transmits data to microcontrollers/flight controllers. The micro-controllers/flight controllers will analyze the data received from sensors and send the commands to the motor driver regarding speed and direction of rotation of motors. The motor driver will send the appropriate voltage and power ratio to the motors according to the commands of micro-controllers. The BLDC motors will rotate in the desired speed and direction guided by micro controllers. The impellers are connected to the motors will rotate by the movement of motors in all directions. The speed and direction (clock wise and anti-clockwise) of rotation of impeller determines the flight, stability and turning of drone. The remote with keys will act as wireless communication system (WCS) for sending and receiving commands to the microcontrollers. This whole entire system of is considered as IOT system is shown in Fig.  6 .

a Conceptualize power and work flow in components of drone; b systematic workflow of IOT system in drone

Overview of internet of things (IOT) employed in drones

2.2.1 Battery

Mostly in UAVs LiPo batteries are mainly used to supply power to the electronic components like sensors, micro-controllers, motors and cameras etc. due to the advantage of light weight, high voltage polarity [ 24 , 25 ].

2.2.2 Sensors

The function of sensor is to collect the date of the physical quantity and transmit then in form of signals to micro-controller [ 26 ]. Sensors will transmit signals in two types digital and analog signals. Analog signals are the continuous (temperature, light intensity, accelerometer, gyroscope, speed and direction sensors etc. which are vary with time) and digital signals are discrete (leaf moisture, soil Nitrohen Phosparaus and potasisum (NPK) content and potential of hydrogen (pH) sensors which provide constant physical output) [ 27 ]. In UAVs both the analog and digital sensors were used. For the motion, flight and stability analog sensors are used whereas for measurement of any physical quality like NPK content in soil digital sensors are used [ 28 , 29 ]. The various types of sensors employes in UAVs are shown in Fig.  7 and Table  3 .

Data flow diagram of drone by employing various sensors

2.2.3 Micro-controller/flight controller

Microcontroller or system of chip (soc) which acts as processing and controlling unit electronic system. It includes a processor to process the sensor data, memory to store the sensor data for visualisation and input/output (I/O) pins to connect input (sensor) devices and output (motors, screen etc.) devices [ 45 ]. Among them analog pins serve as PWM pins to control the speed of dc motor, digital pins serve as direction pins to control the clockwise and anticlockwise rotation of dc motor, and USB Port to upload the Programme from PC [ 5 , 46 ]. The data transmitted can be displayed in by connectivity gateways like Bluetooth, WiFi etc. In UAVs, the flight controller contains the microcontroller as part of its hardware, but the microcontroller itself is a general-purpose component used for other tasks. In summary, a drone requires both a microcontroller and a flight controller [ 47 , 48 ]. The microcontroller handles general computing tasks and interfaces with various components, while the flight controller specifically manages flight, controlling motors, and safe aerial operation of drones [ 26 , 49 ]. The various types of micro-controllers/flight controllers are used in IOT and UAVs are given in Table  4 .

2.2.4 Connectivity/gateway systems

A Connectivity/Gateway is the signal transmitting and receiving unit between the micro-controller unit and remote [ 59 , 60 ]. It enables wireless communication between the drone and a remote controller for data transfer, configuration, or software updates [ 61 ]. The prominent connectivity gateways are used in drones are given in Table  5 . Among all RF, IR gateways are used for indoor purpose to control indoor devices like Television, Ac etc. [ 62 , 63 ]. The Bluetooth and WiFi gateways are used for outdoors to control UGVs and UAVs [ 64 , 65 , 66 ]. Whereas GSM are used for controlling UGVs, UAVs and robots from larger distances over miles [ 67 ]

2.2.5 Motor

The most prevalent usage of motors in drones is to allow multiple rotor drones to fly by spinning their propellers. These propellers spin by the rotation of the motors rapidly to generate thrust and lift, enabling the drone to stay airborne. Brush motors are utilized in smaller drones, whereas brushless motors are used in large drones [ 26 , 53 ].

2.2.6 Motor drivers/controllers

Motor driver/controller will regulate the rotational speed of DC motor by Pulse Width Modulation/speed modulation (PWM) pin and direction of rotation by Direction (DIR) pin in connection with micro-controller as shown in Fig.  8 [ 54 , 84 ].

Controlling system of micro-controller to DC motor

2.2.7 Cameras

Drone cameras serve various essential purposes, revolutionizing the way capture visual data from the air. Drone cameras play a pivotal role in diverse fields, enabling efficient data collection, visualization, and analysis [ 66 ]. The various types of cameras used in drones and their applications in farm operations are shown in Fig.  9 and given in Table  6 .

Cameras types and their applications in farm activities. a Visual camera, b multispectral camera, c thermal camera and d LiDAR cameras

2.3 Degree of freedom (DOF)

The dof give UAVs the ability to maneuver effectively in various environments and perform a wide range of tasks, including surveillance, reconnaissance, mapping and delivery. By controlling these movements, operators can navigate UAVs through complex airspace, avoid obstacles, and achieve mission objectives with precision and efficiency [ 66 , 102 ]. The 5 dof (3 translational and 2 rotational) motions of drone is shown in Fig.  10 and the conditions of rotation of impellers w.r.t movement of drone in various direction is given in Table  7 .

DOF of rotation of the drone in XYZ axis

2.3.1 Working principle and flow pattern

When air flows over an aerofoil and pressure, viscous and drag force act on the profiles. High fluid pressure at the bottom and low pressure at the top of the propeller causes an upward force which is called a lift. This force is responsible for lifting the weight of a drone. The amount of lift force depends on the angle of inclination of the aerofoil or propeller [ 15 , 103 ]. The working principle of flying mechanism is shown in Fig.  11 .

Working principle of flying mechanism in drone

For each propeller, speed and direction of rotation are independently controlled for balance and movement of the drone. To maintain the balance of the system, one pair of rotors rotates in a clockwise direction and the other pair rotates in an anti-clockwise direction. To move up, all rotors should run at high speed. By changing the speed of rotors, the drone can be moved forward, backward, and side-to-side [ 104 , 105 , 106 ].

2.4 Applications of UAVs in agriculture

The UAVs play a vital role in modern agriculture by revolutionizing the below-mentioned activities is shown in Fig.  12 . There are many case studies states that the usage of drones in the following applications obtained better results than the traditional practices. A study conducted in United States, states that drones equipped with multispectral sensors were used to monitor crop health. The data collected allowed farmers to identify areas affected by pests, diseases, and nutrient deficiencies with high precision, leading to targeted interventions that improved crop yields by up to 20% [ 21 ]. Lelong et al. [ 107 ] employed UAVs for assessing wheat crop status through high-resolution aerial imagery which estimates biomass, nitrogen status, and grain yield. In Spain, UAVs have been employed for olive grove management. For instance, after the 2019 floods in India, drones were deployed to survey affected farmlands, providing detailed maps that helped in distributing aid and planning replanting efforts effectively [ 47 ].

Application of drone in smart farming

2.4.1 Crop monitoring and health assessment

UAVs equipped with advanced imaging technologies, such as multispectral and hyperspectral cameras have proven effective in assessing crop health. These drones can capture high-resolution images that allow for the identification of plant stress, nutrient deficiencies, and pest infestations [ 94 , 108 , 109 ]. The studies show that UAVs can cover large areas quickly, providing farmers with timely data to make informed decisions about crop management [ 110 ]. Early identification of such issues allows farmers to take timely actions and implement precision agriculture practices, optimizing resource usage and increasing crop yields [ 111 ]. Hunt et al. [ 19 ] utilized UAVs to monitor canola crops. The aerial imagery allowed for the identification of infected plants at an early stage, enabling targeted fungicide applications. This approach reduced the overall fungicide use by 30% and increased crop yields by 10%. A study conducted in In Costa Rica, UAVs were used to map and monitor reforestation projects. Drones provided high-resolution images and data on tree growth and health, which were crucial for assessing the success of reforestation efforts [ 112 ].

2.4.2 Precision application of inputs

Inputs such as herbicide, fertilizers, and pesticides, farmers can optimize resource utilization while minimizing over usage and environmental impact [ 113 , 114 ]. Integrated GPS technology and automated systems allow drones to execute tasks with unprecedented accuracy, resulting in cost savings and environmental sustainability [ 115 , 116 ]. In Japan, rice farmers have adopted UAV technology for aerial seeding and pesticide application. A study conducted over three growing seasons showed a significant reduction in labor costs and time, with drone-assisted farming reducing the need for manual labor by 50% and pesticide usage by 30%, resulting in higher profit margins [ 18 ]. Farmers using drones for soil analysis, planting and spraying reported a 25% reduction in input costs, including seeds, fertilizers, and pesticides, while maintaining or increasing crop productivity [ 117 ].

2.4.3 Field mapping and analysis

Drones equipped with mapping software and GPS technology can create accurate 3D maps of fields, helping farmers to assess field topography, drainage patterns, and soil variability. The empirical tests have demonstrated their ability to create detailed topographical maps and assess field variability, which is crucial for implementing precision agriculture techniques. This mapping capability supports better planning and resource allocation on farms [ 118 ]. Moreover, this data aids in creating site-specific management strategies and guiding irrigation and drainage planning [ 119 ]. A study by Mulla [ 120 ] demonstrated the effectiveness of UAVs in creating detailed soil maps using multispectral imagery. These maps helped farmers understand soil variability within their fields, allowing for precise application of fertilizers and amendments. This precision agriculture approach resulted in a 15% increase in fertilizer efficiency and a 12% increase in crop yields.

2.4.4 Crop spraying

Drones equipped with sprayers can efficiently and evenly apply pesticides, herbicides and weedicides over large areas. The ability to fly at low altitudes and follow precise flight paths allows for more uniform coverage, reducing chemical wastage and labor costs. Recent advancements in UAV technology include the development of automated spraying systems. These systems can autonomously navigate fields and apply inputs based on pre-defined parameters, improving efficiency and reducing labor costs. Field tests have shown that automated UAVs can perform these tasks with high precision, leading to better resource management [ 121 ]. Moreover, accurately it was found that the Aerial spraying by UAV can be 5times faster than the conventional spraying [ 122 , 123 ]. Booysen et al. and Pansy et al. [ 124 , 125 ] states UAVs with hyperspectral imaging sensors were used to detect early signs of pest infestation and nutrient deficiencies in olive and mango trees. The early detection allowed for timely interventions, olive production by 18% and reduced chemical use by 25%.

2.4.5 Planting and seeding

Drones with seed dispensers and robotic arms enables accurate planting depth, reduces missing and multiple losses and facilitates precision planting. This can be especially useful for re-seeding areas that are difficult to access or for planting cover crops to improve soil health. The study [ 126 ] demonstrates the use of UAVs in precision livestock farming on a mixed-use agricultural farm in India. The drones were equipped with high-resolution RGB cameras and thermal imaging sensors to monitor the health and activity of cattle. As a result, the farm saw a 30% reduction in health-related livestock losses and a 20% improvement in overall herd productivity. Additionally, the use of UAVs reduced the need for manual health checks, saving labor costs and time.

2.4.6 Livestock monitoring

Drones with thermal cameras can be used to monitor livestock by their respiration temperature in large pastures [ 127 ]. This helps farmers to identify health issues, locate missing animals, and optimize grazing patterns [ 128 ]. [ 129 ] found that UAVs could effectively identify sick or injured animals, improving herd management and reducing mortality rates by 12%. The ability to monitor livestock remotely also decreased the need for labor-intensive field checks, significantly reducing operational costs. A study by [ 127 ] demonstrated the effectiveness of UAVs in automated livestock monitoring on a cattle farm in Spain. This approach resulted in a 25% reduction in veterinary costs and a 20% decrease in cattle mortality rates, highlighting the potential of UAV technology in enhancing the efficiency and effectiveness of livestock farming operations.

2.4.7 Irrigation management

Drones with thermal, multispectral cameras and soil moisture, crop moisture and temperature sensors can identify crop water stress levels, evapotranspiration and leak detection. This helps farmers in optimizing irrigation schedules and water efficiency [ 38 , 94 ]. Similarly, in Australia, vineyards have benefited from UAVs with thermal cameras helped identify water stress in vines, allowing for precise irrigation management. This led to a 15% increase in grape quality and a 10% reduction in water usage [ 130 ]. Similarly, Berni et al. [ 131 ] demonstrated the use of UAVs equipped with thermal and multispectral cameras to monitor water stress in crops which enables farmers to optimize irrigation schedules, resulting in a 20% reduction in water use without compromising crop yield.

2.4.8 Crop harvesting

Drones with GPS cameras, blades, motors and impellers as attachments helps in automated crop harvesting, particularly in horticulture and orchard crops [ 18 ]. These are potential to reduce labor costs and increase harvesting efficiency [ 108 ]. A study by van der Merwe et al. [ 132 ] demonstrated the use of drones in rice harvesting. UAVs equipped with cutting mechanisms were used to harvest rice in paddy fields. The drones operated autonomously, following pre-programmed routes to ensure even and thorough harvesting. This method reduced the time and labor required for harvesting, particularly in large fields. Canicattì and Vallone [ 133 ] reported 40% reduction in harvesting time and a 25% increase in overall efficiency, by the usage drones in vegetable crops harvesting.

2.4.9 Soil and field analysis

Drones mounted with moisture, N.P.K, sensors, robotic arms can collect the soil sample and evaluate content in the soil, soil erosion, nutrients content, and fertility of the soil [ 134 , 135 , 136 ]. This collected data helps farmers in decision making of cropping pattern and type of crop to be sown which saves time and resources [ 137 , 138 ]. A study by Zhou et al. [ 139 ] demonstrated the effectiveness of UAVs in creating detailed soil maps using multispectral imagery. These maps helped farmers understand soil variability within their fields, allowing for precise application of fertilizers and amendments. This precision agriculture approach resulted in a 15% increase in fertilizer efficiency and a 12% increase in crop yields [ 140 ].

2.4.10 Climate assessment

Drones equipped with temperature, humidity, moisture, pressure, wind-speed and rainfall sensors will detect upcoming weather conditions. Advance notice of storms or lack of rain can be used to plan the crop [ 141 ]. A study by Dandrifosse et al. [ 142 ] demonstrated the effectiveness of UAVs in assessing crop damage caused by extreme weather events. The drones were equipped with high-resolution cameras and thermal sensors to capture detailed images of agricultural fields after incidents of heavy rainfall and strong winds. By analyzing these images, the researchers could identify areas of the fields that suffered from waterlogging and wind damage [ 143 ].

2.4.11 Geofencing

Drones with thermal cameras can easily detect animals or human beings. For crop planning, planting, and harvesting to boost yield, it is frequently employed in agronomy [ 144 ]. A study by Zhang et al. [ 145 ] demonstrated the effectiveness of UAVs equipped with geofencing technology in protecting crops from wildlife damage. The drones created virtual boundaries around agricultural fields, detecting and deterring animals such as deer and wild boars. When animals approached the geofenced areas, the drones automatically deployed auditory and visual deterrents to scare them away. This approach reduced crop damage by 30% and increased overall crop yield by 10%.

2.4.12 Thermal imaging

Application of Thermal imaging technique in agriculture includes greenhouse monitoring, irrigation scheduling, plants disease detection, estimating fruit yield, evaluating maturity of fruits [ 146 , 147 ]. The studies [ 87 , 148 ] states that UAVs with thermal imaging to monitor soil moisture levels in rice fields in India. The thermal maps helped identify areas with varying soil moisture, enabling precise irrigation practices. This precision agriculture approach led to a 25% reduction in water usage and a 15% increase in rice yields, demonstrating the benefits of thermal imaging for sustainable water management in agriculture.

2.4.13 Crop count and plant emergence analysis

UAVs with LiDAR cameras, AI and ML algorithms are a useful for the estimation of tree/crop, obtaining data on crop emergence, drive replanting decisions and help predict yield. This system produces 97% accuracy in its output using data obtained with drones and Photogrammetry [ 149 , 150 ]. A study by Kakarla et al. and Amarasingam et al. [ 151 , 152 ] utilized drones equipped with high-resolution cameras and machine learning algorithms to count maize and sugarcane plants and predict yield in fields. The drones captured imagery during critical growth stages, analyzing plant density and health indicators such as Normalized Difference Vegetation Index (NDVI). This approach enabled accurate estimation of crop yield potential before harvest, facilitating informed decision-making for crop management practices [ 153 ]. Awais et al. [ 154 ] stated drones equipped with thermal and multispectral sensors were employed to monitor plant emergence dynamics in soybean fields. The study focused on early-season monitoring of seedling emergence rates and uniformity across large agricultural plots. Thermal imaging helped detect variations in soil temperature affecting germination, while multispectral data provided insights into crop health and growth stages.

2.4.14 Real time data processing

The incorporation of real-time data processing algorithms, including artificial intelligence, edge computing in UAVs allows for real-time data analysis directly on the field [ 155 , 156 ]. This capability enables immediate decision-making, such as adjusting irrigation or applying fertilizers based on current crop conditions The reduction in data processing time significantly enhances the responsiveness of farming operations and effectiveness of UAVs [ 94 , 118 ].

In summary, the deployment of UAVs in agriculture has led to numerous benefits, including enhanced crop monitoring, efficient resource management, improved livestock health, and sustainable farming practices. The empirical testing of UAV hardware in precision agriculture has yielded promising results, showcasing their effectiveness in enhancing crop monitoring, pest management, and overall farm efficiency. As technology continues to evolve, the integration of UAVs into farming practices is expected to play a vital role in addressing the challenges of modern agriculture, including sustainability and productivity.

2.5 Limitations of UAVs

Farmers may exhibit significant resistance to integrating UAV technology with traditional farming practices due to several key factors. Primarily, the high initial cost of UAVs and associated technology can be prohibitive for many farmers, especially smallholders operating on thin margins. This financial burden includes not only the purchase of the drones themselves but also the necessary software, training, and ongoing maintenance. Additionally, there is often a steep learning curve associated with operating UAVs and interpreting the data they generate, which can be daunting for farmers who are accustomed to conventional methods and may lack technical expertise. There are also concerns about data privacy and security, as the use of drones involves capturing detailed imagery of their land, which farmers may be hesitant to share due to fears of data misuse or breaches [ 157 ].

Cultural and generational factors can also play a role in resistance, with older farmers potentially more reluctant to adopt new technologies compared to younger. Additionally, the regulatory environment surrounding UAV usage can be complex and restrictive, further deterring adoption. To overcome these barriers, it is crucial to provide comprehensive support, including subsidies or financing options, training programs, clear demonstrations of the technology’s benefits, and assurances of data security to build trust and encourage farmers to embrace UAV technology [ 158 , 159 ]. The Limitations of UAVs are shown in Fig.  13 .

Limited payload capacity: agricultural drones have a restricted payload capacity carrying heavy sensors or additional equipment may not be feasible. This limitation affects the types of tasks drones can perform [ 157 , 160 ].

Limited flight time: drones have a restricted battery life, which directly impacts their flight duration. The flight time is determined by battery capacity and power limitations. This limitation can be challenging when covering large agricultural areas efficiently [ 157 ].

Weather constraints: drones cannot operate in certain adverse weather conditions like strong winds, heavy rain, snow, fog and extreme temperatures. Drones may not be able to fly in winds exceeding 10 m/s (22 mph) as specified by many manufacturers. Strong winds and turbulence can cause loss of control, instability, and potential crashes. Rain, snow, and hail can damage drone electronics, cause short-circuits, and lead to crashes [ 142 ]. Precipitation can reduce visibility and cause loss of communication between the drone and controller. Fog, clouds, and haze can reduce visibility, causing collisions with obstacles and loss of control. Moreover, condensation on camera lenses in clouds can also impair visibility. Hot temperatures over 100°F (38 °C) can cause overheating and loss of video feed. Cold temperatures can reduce battery life and flight time. Ice buildup on propellers and wings in cold weather increases power consumption, reduces lift and thrust, and leads to crashes [ 161 ].

Data processing challenges: managing and interpreting the large amounts of data collected by drones can be challenging for farmers, requiring specialized skills and tools for effective analysis [ 162 , 163 , 164 ].

High initial costs: drones equipped with hardware components advanced sensors, cameras, micro-controllers and remote-controllers come at high prices. Software’s required for interpretation; visualization of large data are also expensive leads to increase in overall initial cost. Farmers need training to operate drones effectively. Certification from the Federal Aviation Administration (FAA) is often required for usage of drone, which involves additional costs. Regular maintenance and occasional repairs are necessary to keep the drone in optimal condition which leads to increase in maintenance and operational cost. The initial cost of purchasing drones, along with ongoing maintenance and operator training, can be prohibitively high for many farmers, especially small and medium-sized operations in rural areas. In India most of the farmers are small and marginals farmers [ 165 , 166 ].

Regulatory restrictions: strict regulations and legal requirements vary across different regions and countries. Licensing, permits, and operational restrictions can hinder the full potential of drones in agriculture. Farmers need to navigate these regulations to use drones effectively [ 159 , 167 , 168 ].

Airspace restrictions: in India, the Directorate General of Civil Aviation (DGCA) has designated agricultural areas as Green Zones, which exempts registered drones from flight restrictions below 120 m (approximately 400 feet). It’s crucial for farmers using drones in agriculture to understand and comply with all relevant airspace regulations to ensure safe and legal operations. Flying over military bases, airports, and other restricted areas is not allowed, even with a commercial license. Airports have restricted airspaces with limited access for drone flights. In controlled airspaces like Class B, C, D and E airspaces Air Traffic Control (ATC) permissions are need to fly drones [ 169 , 170 ].

Privacy concerns: drones can capture high-resolution images and videos, raising privacy concerns. People on the ground may feel uncomfortable with aerial surveillance. Balancing the benefits of data collection with privacy rights is essential [ 171 , 172 ].

Skills and training facilities: farmers need proper training and certifications to legally operate drones, which adds to the barriers to adoption. Pilots need to understand flight controls, data collection, and data analysis [ 173 ].

Durability: along with the connectivity challenges, the durability of the sensors, micro-controllers and IoT system should be able to deal with real-time farming conditions [ 174 ].

Reliability: reliability is another problem for real-time farm monitoring because there is no provision to recover the data in the field of nodes and sensors after they stop working [ 175 , 176 ]. There can be several problems in a real-time working environment, such as extreme weather change conditions, bird or insect attacks, and other technical problems. So, the IoT system should be more reliable in the upgraded versions [ 177 ].

Limitations of UAVs in smart farming

2.6 Future prospects

To overcome the limitations and making drones more accessible to the farmers globally. The following future prospects are provided below.

2.6.1 Government policies and tax incentives

Globally, high Goods and Services Tax (GST) and import duties considerably increase the cost of imported drone parts. For example, in India, GST adds 18%, while in the European Union, import duties range from 2 to 10%, and in the U.S., they are around 0% to 2.5%. This can elevate the total cost of drones by 20% to 30% [ 178 ]. Countries such as China and the U.S., which have robust domestic production capabilities for electronic components, benefit from reduced costs and competitive pricing. In contrast, many other countries import essential drone parts and then assemble them, leading to higher overall expenses due to added taxes and duties. To enhance affordability and support local innovation, it is recommended that investments be directed toward domestic semiconductor and electronic component manufacturing, policies be developed to incentivize local production, and industry partnerships be encouraged to meet the specific needs of drone technology. The local government subsidies and grants can significantly lower the initial costs of unmanned aerial vehicles (UAVs) for farmers, making the technology more accessible, particularly for small to medium-sized agricultural operations. To further support this initiative, implementing incentives such as subsidies, tax breaks, and grants for electronic components manufacturers can stimulate investment and drive development in this sector, ultimately promoting local production and reducing overall costs for UAVs [ 179 ].

2.6.2 Training program incentives

The cost of training programs for drone operation varies globally, from $500 to $2000 per participant. This range depends on factors such as the program’s comprehensiveness, duration, and the inclusion of certification. Supporting farmers with training programs under government schemes is a vital step towards increasing the adoption of drones in agriculture. Reducing training costs and equipping farmers with essential skills through government-supported programs can significantly enhance the integration of drones into agricultural practices, thereby improving productivity and efficiency [ 180 , 181 ].

2.6.3 Data processing algorithms

Expanding the workforce in artificial intelligence (AI), machine learning (ML) and image processing (IP) algorithms are crucial for handling and advancing data processing in agricultural drones. Globally, the U.S. has seen a rise in AI and ML job opportunities, reflecting a broader trend of increased investment in these fields in the European Union and Asia, where countries like China and India are focusing on enhancing AI and ML expertise to support technological advancements. To maximize these benefits, it is recommended that governments and industry stakeholders invest in education and training, foster collaboration between academia and industry, and support innovative startups and companies working on cutting-edge AI and ML solutions for drone data processing. By improving data handling capabilities, developing advanced software solutions, and reducing operational costs, a skilled AI and ML workforce can significantly enhance the effectiveness and affordability of drone technology in agriculture [ 182 , 183 ].

3 Conclusion

In the domain of smart farming, UAVs have huge potential. Farmers now mainly employ UAVs for spraying applications, which was a significant step forward in transitioning agriculture 3.0 to agriculture 4.0. UAVs can be used for a variety of farm activities other than spraying, such as soil testing, field mapping, crop harvesting and crop health monitoring. Farmers can optimize agricultural productivity by employing advanced sensors to monitor key environmental factors such as rainfall, moisture levels, and temperature. In the livestock industry, UAVs are able to track animals’ health and diagnose infections. The UAVS additionally enables considerably more accurate examinations of soil water and nitrogen levels. Despite these advantages, adopting UAVs in agriculture on larger scales offers serious challenges. The costs associated with purchasing and maintaining sophisticated hardware and software pose hurdles. Furthermore, farmers in rural areas frequently lack the fundamental knowledge about employing drone technology. Research papers investigate numerous strategies and tactics to improve the usage UAVs for automation in agriculture. The articles include UAVs along with IoT technologies, as well as protocols and their functionality.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

• Unmanned arial vehicles

Internet of Things

Information and communication technology

Wireless communication system

Remote sensing

Machine learning

Million tonnes

Global System for Mobile Communication

Wireless fidelity

Dynamically remotely operated navigation equipment

Global positioning system

Geological information system

Visualizing scientific landscapes

Degree of Freedom

Red green blue

Global system for mobile communications

Brushless direct current

Light detection and ranging

Arduino integrated development environment

Lithium polymer

Nitrogen, phosphorus, potassium

Potential of hydrogen

Soil electrical conductivity

Kilo bytes per second

Mega bytes per second

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Berni J, Zarco-Tejada PJ, Suarez L, Fereres E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens. 2009;47:722–38. https://doi.org/10.1109/TGRS.2008.2010457 .

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Canicattì M, Vallone M. Drones in vegetable crops: a systematic literature review. Smart Agric Technol. 2024;7: 100396. https://doi.org/10.1016/j.atech.2024.100396 .

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Javaid M, Haleem A, Khan IH, Suman R. Understanding the potential applications of artificial intelligence in agriculture sector. Adv Agrochem. 2023;2:15–30. https://doi.org/10.1016/j.aac.2022.10.001 .

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Acknowledgements

The present review work was supported by Department of farm machinery and power engineering (FMPE), College of Technology and Engineering (CTAE), Maharana Prathap University of Agricultural and Technology (MPUAT), Udaipur, India.

This study had no funding from any source.

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Swetha Makam, Bharath Kumar Komatineni & Sanwal Singh Meena

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SM: data curation, methodology, writing—original draft of the manuscript. BKK: conceptualization, software, validation, visualization, writing—review and editing. SSM: supervision, writing—review and editing. UM: data curation, writing—review and editing. M.S: writing—original draft preparation, methodology, data curation, software.

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Makam, S., Komatineni, B.K., Meena, S.S. et al. Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discov Internet Things 4 , 12 (2024). https://doi.org/10.1007/s43926-024-00066-5

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CBSE Class 10 Information Technology Sample Paper 2024-25 with Marking, Download in PDF

Cbse sample paper and marking scheme 2025: cbse has released the sample paper of information technology skill subjects for class 10 for the 2025 board exams. download the information technology sample question paper with marking scheme in pdf here. .

CBSE Class 10 Information Technology Skill Subject Sample Paper 2025: The Central Board Of Secondary Education (CBSE) has made sample paper available for the all subjects for class 10 on its official website. These sample papers help them to practice and perform better in examinations. In this article we have provided the sample paper for the CBSE Class 10 Information Technology sample paper 2025 , along with the section wise questions and direct link to download the sample paper to prepare and practice. For now, students can take a look at the Skill Subject Sample Paper. Read the complete article to download the free PDF of the Information Technologys sample paper and the marking scheme as well.

Also Check:  CBSE Class 10 Sample Paper 2024-25 For Skill Subjects: Download FREE PDFs

CBSE Class 10 Information Technology Skill Subject: General Instructions:

CBSE Class 10 Information Technology Sample Question Papers 2024-25 

Section a: objective type questions .

Q. 1 Answer any 4 out of the given 6 questions on Employability Skills (1 x 4 = 4 marks)

1. What is the importance of effective communication in a workplace?

(a) Reducing office expenses (b) Enhancing team collaboration and productivity

(c) Increasing employee vacation days (d) Improving office aesthetics

2. High expectations from self can leave with chronic anxiety and stress, thus leading to  _____________ stress.

(a) Physical (b) Emotional (c) Social (d) Financial

3. John notices that the cost of raw materials is lower in a different supplier's location  compared to his current supplier. He decides to switch to the new supplier to save money. Which function that the entrepreneur is doing?

(a) Makes decisions

(b) Divides income

(c) Takes risk

(d) Innovation

4. Imagine you are working on a challenging project for your studies, and you have a tight deadline. No one is around to cheer you on or offer encouragement, but you need to  push through and complete the work. What will make you complete work without others cheering you?

(a) Self-confidence

(b) Communication

(c) Self-motivation

(d) Self-esteem

5. Which organization has adopted the Sustainable Development Goals.

(a) UNICEF (b) League of Nations

(c) United Nations (d) World Health Organization

6. What should you do to ensure secure online transactions?

(a) Lock your computer

(b) Give credit card or bank details only on safe websites

(c) Use anti-virus

(d) Do not use pirated software

Q. 2 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)

1. What is a style in LibreOffice Writer?

a) A method of typing b) A collection of all formatting information

c) A tool for drawing shapes d) A spell-check feature

2. Which of the following is an example for absolute cell referencing?

(a) C5 (b) $C$5 (c) $C (d) #C

3. Which of the following tabs is by default active when the Table of Contents, Entries or  Bibliography dialog box is opened?

(a) Entries

(b) Background

4. Which of the following is true about Track Changes feature of Writer?

(a) You cannot record a change made in the document.

(b) A comment of a particular author only can be deleted

(c) Any change made to the document is permanent.

(d) None of the above 

5. Imagine you are a financial analyst tasked with analyzing quarterly sales data for a  multinational corporation. The data is stored in separate sheets within an Excel  workbook, each representing sales figures from different regions (e.g., North Zone, East  Zone, South Zone). How can you efficiently view and compare quarterly sales data from multiple  regions in a single spreadsheet to identify trends and relationships?

(a) By creating separate charts for each region’s data.

(b) By using the Consolidate function to combine information from all regional sheets into one summary sheet.

(c) By manually copying and pasting data from each region’s sheet into a new sheet.

(d) By deleting unnecessary data from each region’s sheet.

6. Sore lower back is caused due to _________________.

(a) reaching forward frequently

(b) no lumbar support

(c) no upper back support from chair

(d) reaching forward for long periods 

Q. 3 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)

1. What is the extension of spreadsheet file in Calc?

(a) .odb (b) .odt (c) .odg (d) .ods

2. Which of the following is the shortcut key to open the Templates dialog box?

(a) Ctrl+Alt+N (b) Ctrl+Shift+N (c) Ctrl+Alt+T (d) Shift+Alt+T

3. Which style category would you use to format a section containing text, graphics, and  lists?

a) Page Style b) Paragraph Style c) Character Style d) Frame Style

4. It is a reference point for the graphics which is created while positioning any image. This point could be the page, or frame where the object is either a paragraph, or even a  character in a word processor.

(a) Wrap Text (b) Anchoring (c) Alignment (d) BookMark

5. Which of the following is an invalid Macro Name?

(a) 1formatword

(b) format word

(c) format*word

(d) Format_word

6. A fresh food cafeteria helps to maintain the ______________ of the employee.

(a) Health (b) Morale (c) Productivity (d) Engagement

Q. 4 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)

1. Which of the following feature is used to jump to a different spreadsheet from the current spreadsheet in LibreOffice Calc?

(b) Hyperlink

(c) connect

2. Which of the following operations cannot be performed using LibreOffice Calc?

(a) Store and manipulate data

(b) Create graphical representation of data

(c) Analysis of data

(d) Mail merge

3. The details associated with an entity are called ____________.

(b) Attributes

(c) Records 

(d) Primary key

4. The _____________ data is a combination of letters, numbers or special characters.

(a) Structured (b) Unstructured (c) Semi-structured (d) Alphanumeric

5. Which kind of hazards can occur in IT industry?

(a) Biological

(b) Chemical

(c) Physical

(d) Ergonomic

6. In a Query Design wizard, which of the following buttons is clicked to move a field from ‘Available fields’ list box to ‘Fields in the query‘ list box?

(a) > (b) <9 (c) ∨ (d) ∧

Q. 5 Answer any 5 out of the given 6 questions (1 x 5 = 5 marks)

1. Identify the mode, where we can modify in the structure of table?

a. Datasheet view b. Structure view c. Design view d. All of the above

2. What is the primary purpose of a query in a database?

(a) To enter new records (b) To create reports

(c) To retrieve specific data (d) To design forms

3. Which of the following is NOT true about forms?

(a) It is the front end for data entry

(b) It can contain text fields

(c) Graphics can be inserted on the form

(d) It can accept only fixed number of records

4. For an organisation, the proper security procedures will reduce ________________.

(a) liabilities (b) insurance (c) business revenue (d) operational charges of the company

5. Which of the following is not an example of ignition sources of open flames?

(a) Gas ovens (b) Lighters in smoking areas (c) Welding torches (d) space heaters

6. Which action contributes to a healthy and safe working environment?

(a) Keeping emergency exits clear (b) Leaving cables loose on the floor

(c) Ignoring safety warnings (d) Using unapproved software

To view and access the complete sections, click on the link below to download PDF: 

CBSE Class 10 Information Technology Marking Scheme 2024-25

The marking scheme helps students by giving them the exact idea of what is needed to get good scores and grades in examination. It explains how each answers will be scored, the question weightage for exam, and makes understand what the teacher are looking for in your answer. By knowing the marking scheme students can focus on important topics and practice accordingly and see how well they are doing. To access the marking scheme for class 10 Information Technology sample paper 2025, click on the link below to download the marking scheme in PDF format: 

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Visual Skill Test: Find the different umbrella in the picture in 3 seconds!

NIACL Online Application 2024: एनआईएसीएल स्केल I पदों पर भर्ती के लिए आवेदन शुरू, यहाँ देखें