Artificial intelligence (AI) is impacting every global economic sector including agriculture. The reason AI is becoming more important is because world conditions are rapidly changing and keeping up with those changes requires analyzing massive amounts of data. Analysts from Harbor Research note, “Many farmers today still use many of the same production methods their parents and grandparents used, but farming is under extraordinary pressure from many diverse forces including climate change, the need to produce food sustainably, falling commodity prices, and the rising debt of farmers.”[1] They ask, “Can smart systems, services and the Internet of Things (IoT) seed a much-needed wave of innovation and technology adoption in the farming arena?” The answer to that question should be “yes”; however, no transition from one era to the next comes without cost. Harbor Research analysts explain what happened during the last agricultural transformation:
“With the advent of the industrial age, when society changed from an agrarian base to an industrial base, the way humans worked became more mechanized. Industrial nations built factories and furnished farms with new mechanical tools and systems that were very different from those used in the agrarian era: tractors, steam powered engines, the railroad, telegraph, and much more formed the basis of modern society and business.”
The new mechanized tools that became available to farmers weren’t free; however, they did eventually pay for themselves by improving production. Having moved into a post-Industrial Age, many experts believe the moment has come for the agricultural sector to move with the times and find new ways to enhance production. Harbor Research analysts explain, “Observers have said for more than twenty years that the impacts the information revolution will have on agriculture and food production will be far greater than the impact science had during the transition from an agricultural society to an industrial society.” They then pose another question, “If this is true, then why has the agriculture and farming sector moved so slowly to adopt new digital and information technology?” One answer may be cost. The question should be: Can Information Age technologies increase production enough to make implementing them profitable?
The Benefits of AI in Agriculture
Farmers are, first and foremost, business people. Like other business executives, they need to be able to make a business case for any new technology they purchase. Agricultural sector journalist Gil Gullickson believes information age technologies, especially AI, will prove their value. He writes, “Artificial intelligence promises to bring the same heat to agriculture that [electronic calculators] did to math classes decades ago.”[2] Kent Klemme, general manager of See & Spray for Blue River Technology, told Gullickson, “The biggest advantage of artificial intelligence is the ability to make complex calculations at a high speed that previously required a human to perform. The recent improvements in GPUs [graphics processing units] have provided the computing power to make this possible. It takes a lot of data to target specific problems.”
Gullickson notes, “AI [is] commonly being used across many machinery lines.” AI allows machines to make decisions that previously fell on the shoulders of farmers. Every business, including those in the agricultural sector, make both routine and crucial decisions every day. In fact, Bain analysts Michael C. Mankins and Lori Sherer assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[3] As Klemme noted, with so much data now available, AI is required to make sense of it all. Young So Lee, a Senior Industry Analyst at Frost & Sullivan, adds, “The core essence of digital agriculture practices is to create actionable intelligence and meaningful added value from data analysis beyond the present-day usage of data. The scope of AI technology does not stop at improving productivity by measuring farmland status and ensuring optimal cultivation; AI will also be linked to insurance to manage the risks associated with crop safety.”[4]
AI can also play a valuable role in helping organizations and governments address global food security. Researchers from the Central European University report, “In 2021, 193 million people across 53 countries were acutely food insecure. This number has been steadily increasing during the last few years also as a consequence of the COVID-19 pandemic. To address this global issue, monitoring the situation and its evolution is key. … To tackle this issue, researchers … propose a machine learning approach to predict the number of people with insufficient food consumption when up-to-date direct measurements are not available. … Results show that the proposed methodology can explain up to 81% of the variation in insufficient food consumption. Researchers claim their approach opens the door to food security near-real time nowcasting on a global scale, allowing decision-makers to make more timely and informed decisions on policies and programs oriented towards the fight against hunger.”[5]
Although AI can play a vital role in the agricultural sector, it is not a silver bullet. Rebecca Sakayeda, an agribusiness Marketing Manager at DTN, explains, “It’s important to understand that machine learning, like other technologies, is a part of a process, not a stand-alone solution. AI must provide practical applications that align with existing agricultural operations.”[6] Nevertheless, she notes, AI can be used to improve irrigation and water management; crop modeling; field accessibility; harvest decision support; and weather forecasting.
Concluding Thoughts
The upsides of using AI in agriculture are numerous. Nevertheless, reporters from CNBC note, “Although artificial intelligence has the potential to improve crop management and agricultural productivity, there are substantial risk factors in deploying new AI technologies that are not being considered.”[7] The risks include: “Interoperability; safety and security; reliability of data; and, unintended socio-ecological consequences arising from the use of ML models to optimize yields.” I suspect most, if not all, of those risks are being considered and addressed. That doesn’t mean more research isn’t required. Perhaps the greatest risk is cybersecurity. Harbor Research analysts note, “High performance networks, edge computing, data analytics and machine learning are setting the stage for complex adaptive or autonomous systems in agriculture.” No one wants a hacker to disrupt those types of systems.
There are also continuing disputes over who owns generated data and how it is used. Harbor Research analysts support an open data ecosystem. They explain, “Sensors, devices, drones, combines, storage silos, robotics and more will produce billions of data points about how crops are planted, grown, protected and harvested, increasing visibility and precision in all agricultural enterprises. All this data, however, requires significant integration and data aggregation and management expertise for it to be utilized effectively — a truly open data ecosystem is needed to access the full scope of value created by these smart, connected systems. … Once data is accessible, artificial intelligence and machine learning algorithms can help farmers track crop health and trends to make critical decisions.” Participation from all stakeholders will be required to create a system that works for everyone — from smallholder farmers to the largest agribusinesses.
Footnotes
[1] Staff, “Future Proof Smart Farming,” Harbor Research, 2021.
[2] Gil Gullickson, “Agriculture Embraces Artificial Intelligence,” Successful Farming, 29 November 2022.
[3] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[4] Young So Lee, “Artificial Intelligence (AI) in Agriculture: Global Growth Opportunities,” Yahoo!, 21 September 2022.
[5] Central European University, “Scientists propose machine learning approach to solve the world’s food insecurity issue,” Phys.org, 19 September 2022.
[6] Rebecca Sakayeda, “Artificial Intelligence And Machine Learning In Agriculture Today,” AgriTech Tomorrow, 27 November 2018.
[7] Staff, “What are the risks of using artificial intelligence in agriculture,” CNBC TV18, 16 March 2022.
