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February 23, 2022


One of America’s favorite patriotic tunes celebrates “spacious skies and amber waves of grain.” This pastoral view of America rarely conjures up thoughts about technology and artificial intelligence (AI). In the future, that may change. The editorial team at insideBIGDATA report that a new research study found, “87% of US agriculture businesses are currently using AI technologies.”[1] Lindsay Suddon, Chief Strategy Officer at Proagrica, the firm that conducted the study, notes, “Investment in emerging technologies has been a fixture of the ag sector, even before the pandemic, and these latest figures confirm that this is an industry that is always looking to innovate.” J. Mark Munoz (@jmarkmunoz), a Professor of Management at Millikin University, adds, “Artificial intelligence has offered countless tools for industries around the world to use, including agriculture.”[2] He is also aware that falling for hype can be costly — and farmers don’t have the luxury of wasting money on failed ventures. For that reason, he writes, “In this scenario, there are important questions farm managers need to grapple with: Which AI tools are best to use? How do I use them? Is AI a worthwhile investment? Should I make the move now or wait for a better time after I’ve seen evidences of successful AI models? Is the grass really greener in the world of AI?”


Agriculture and Artificial Intelligence


Suddon believes the grass is greener in the world of AI agriculture. He states, “The ag sector is heading in the right direction in embracing technology to build in greater efficiencies. This bodes well for the next generation of growers, who will expect technological literacy across the sector, which will only bring further success, and therefore more innovation.” Munoz calls technologically-sophisticated agricultural ventures “cognitive farms.” He explains, “Advances in AI and related technologies lead to smart farms or farming models with high cognitive ability.” To support his views, Munoz provides a table showing areas in which cognitive farms are currently partnering with AI firms.


Farming Activities Examples of AI Companies
Weed control Blue River Technology
Harvesting and packaging Harvest CROO Robotics
Diagnosing pests and soil defects PEAT
Soil analysis Trace Genomics
Crop health monitoring SkySquirrel Technologies
Lettuce thinning Blue River Technology
Self-driving tractors Autonomous Tractor Corp
Weather, pests and disease prediction aWhere


Cognitive farms are, by necessity, connected farms. Data must be gathered and transmitted from the field so it can be analyzed for insights. Generally speaking, the more data the better. Suddon notes, “89% of execs feel countries should now share tech and resources, suggesting that we are one step closer to building bridges across the supply chain. Technologies such as AI have the potential to anonymize data while helping to uncover insight that creates new opportunities. Farmers will no longer have to be worried about relinquishing their IP, which will encourage greater collaboration and ultimately growth.” Munoz suggests three ways AI can enhance agricultural operations. They are:


Extensive data capture and analysis — “Farms now have the ability to set up, track and analyze a multitude of data points thereby helping farmers make better decisions. This boosts information accuracy and aids in decision making. For example, a farm manager can use a drone to scan a large track of land and identify the exact location of plant disease or pest infestation in real time.”


Automation and robotics — “In order to speed up manual work or manage manpower shortages, robots are used in farm activities such as fruit picking and lettuce thinning among many others. This can lead to productivity gains with indefatigability, minimization of errors, and consistency of work quality.” For example, tech writer Nick Lavars (@NickLavars) reports researchers at Australia’s Monash University Department of Mechanical and Aerospace Engineering have designed a robot capable of harvesting apples from an orchard at the rate of one apple every 7 seconds.[3]


Predictive analytics — “AI tools have been created to predict changes in weather patterns, pest infestation or soil erosion in order to improve planning and farm management. These tools help farmers take a glimpse of the future and assist them in making informed decisions.”


Journalist Anna Deen (@annadeen2) is so sure that the face of agriculture is changing she writes that when you think about the future of farming you should “think artificial intelligence, robots, and drones.”[4]


Cognitive Farms and Precision Agriculture


As Munoz noted, technology is being used to help farmers and ranchers better understand their land, their animals, and how to use their resources. This is often called “precision agriculture.” However, Deen writes, “There’s one small problem.” That problem, according to Soumik Sarkar, an associate professor of mechanical engineering at Iowa State University, is that “precision agriculture is not that precise.” He explained for Deen, “Although things like GPS currently provide the most efficient route for planting and harvesting, and farmers use lasers to help level the land, even the most tech-savvy farmers lack the ability to, say, target specific crops with pesticides rather than spraying an entire field.” He added, “Optimizing and reducing the usage of water, chemicals, and other resources, while actually growing the amount of crops to feed a growing population with the land we have, is a challenge that you need to pretty much throw the kitchen sink at to solve.”


Sarkar, of course, is referring to an old idiom called the “kitchen-sink approach.” According to the Free Dictionary, the kitchen-sink approach is “an approach to something that involves many different things, often to the point of excess or redundancy. An allusion to the phrase ‘everything but the kitchen sink,’ meaning nearly everything one can reasonably imagine.”[5] Deem reports, “The Artificial Intelligence Institute for Resilient Agriculture at Iowa State University is one of four groups building that proverbial sink with what it calls ultra-precision agriculture. The institute hopes, over the next three to five years, to use data science, machine learning and other artificial intelligence technologies to provide corn and soybean growers with personalized recommendations that will boost crop production. The tools also could help industry scientists develop seeds that deliver higher yields while resisting drought and other stressors.”


John Deere, a company that has been providing technology to the agriculture sector since 1837, recently added their contribution to the kitchen-sink approach: an autonomous tractor. Tech writer Grace Donnelly (@gddonnelly22) reports, “Fully driverless vehicles may still be years away from hitting a street near you, but how about your local farm? John Deere unveiled its first fully autonomous tractor at CES [in January 2022]. While self-driving tractors have been running on farms for at least a decade, the new 8R tractor takes things a step further, allowing farmers to leave the cab and control the machine remotely.”[6] Journalist Will Knight (@willknight) reports, “John Deere’s new 8R tractor uses six pairs of stereo cameras and advanced artificial intelligence to perceive its environment and navigate. It can find its way to a field on its own when given a route and coordinates, then plow the soil or sow seeds without instructions, avoiding obstacles as it goes.”[7]


For now, Deere plans on leasing, rather than selling the autonomous tractors — whose cost could well exceed one million dollars. This arrangement has raised some questions. Knight explains, “Automating more of farming, and adding AI, may stir debate around replacing workers as well as ownership and use of the data it generates.” Ownership of data generated by advanced technologies has been a long-running source of angst in the agricultural sector. Jahmy Hindman, Deere’s Chief Technology Officer, told Knight, “The system will gather data about the soil as it toils away. That information will be used to tweak its algorithms, helping to improve performance and provide farmers with new insights on how to best work their land.” Although he did say, “Farmers can opt out of sharing data.”


Concluding Thoughts


Although most pundits agree that AI-enhanced cognitive farms can immensely benefit the agricultural sector, some worry that technology providers will be the big winners rather than farmers. Critics argue that farmers could end up having to pay for data generated on their property and that loss of data ownership could mean that a farmer’s data could be used by competitors. They also note that many tech providers insist they alone can provide maintenance on their equipment, which, in the long-run could mean farmers have less control over their operations. These challenges will have to be worked out; however, the future of AI in agriculture looks pretty bright. Why? Dmytro Lenniy, Senior Delivery Manager and AgriTech Practice Leader at Intellias, answers that question with another question, “Can you imagine an industry that involves more risk than agriculture?”[8]


Lenniy continues, “We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities. AI can potentially change the way we see agriculture, enabling farmers to achieve more results with less effort while bringing many other benefits.” The biggest challenge introducing new technologies into the agricultural sector is cost. Only the largest agri-businesses can afford most of the technologies being introduced. Family-owned farms and small sharecroppers around the world are largely priced out of many the breakthroughs being introduced. Nevertheless, the world needs every one of these crop producers to feed the world.


Another reason the world requires widespread implementation of these new technologies is that the agricultural sector continues to lose workers. That’s not surprising. The work is hard and often conducted in harsh conditions without much remuneration. Automation can help relieve the worker shortage and increase production. If the global food supply chain is to remain vibrant and efficient, how this automation is implemented will require the cooperation of all stakeholders


[1] Editorial Team, “87% of US Agriculture Businesses Are Currently Using AI,” insideBIGDATA, 25 November 2021.
[2] J. Mark Munoz, “AI in Agriculture: Is the Grass Greener?” California Management Review, 30 March 2020.
[3] Nick Lavars, “Apple harvesting robot plucks a piece of fruit every 7 seconds,” New Atlas, 27 April 2021.
[4] Anna Deen, “The future of farming? Think artificial intelligence, robots, and drones.” Fix Solutions Lab from Grist, 13 January 2022.
[5] Staff, “kitchen-sink approach,” The Free Dictionary.
[6] Grace Donnelly, “John Deere thinks its self-driving tractor can help feed the world,” Emerging Tech Brew, 7 January 2022.
[7] Will Knight, “John Deere’s Self-Driving Tractor Stirs Debate on AI in Farming,” Wired, 4 January 2022.
[8] Dmytro Lenniy, “Artificial Intelligence in Agriculture: Rooting Out the Seed of Doubt,” Intellias Blog, 19 October 2021.

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