John Lovett | October 5, 2026
https://www.pbcommercial.com/ai-in-agriculture-experts-say-human-judgment-remains-key-astechnology-advances/
As artificial intelligence becomes more powerful and increasingly capable of handling tasks once requiring specialized skills, agricultural researchers say the human ability to think critically, recognize mistakes and ask meaningful questions will become even more valuable.
That was among the central themes of a roundtable discussion held during the AI in Agriculture Symposium on Sept. 21. Panelists discussed how rapidly advancing AI technology is changing agriculture, scientific research and the skills universities need to develop in students preparing to enter the workforce.
The symposium was hosted by the Center for Agricultural Data Analytics, which is part of the Arkansas Agricultural Experiment Station, the research arm of the University of Arkansas Division of Agriculture.
AI is already making its way onto farms, although producers may not necessarily encounter it as an entirely new piece of technology.
Jason Davis, an assistant professor in the Department of Crop, Soil and Environmental Sciences and a remote sensing and pesticide application extension specialist for the Division of Agriculture, said much of the technology is being incorporated into equipment and systems farmers already know how to operate.
“From my perspective, it has been primarily in row crops, embedded in systems they are already familiar with,” Davis said. “It’s a dashboard they’re already using, or a computer monitor in the cab of their tractor. It’s suddenly expanded utility and capability.”
The discussion went beyond how farmers are using AI and examined what the technology means for researchers and future agricultural professionals.
Tasks such as computer coding, data analysis and website development can increasingly be completed with assistance from AI. Panelists said that shift does not eliminate the need for human expertise. Instead, it changes where that expertise becomes most important.
Researchers still must understand the science behind their work, recognize when AI produces questionable results and decide which problems are important enough to pursue.
Arthur F.A. Fernandes, a geneticist for Cobb-Vantress, compared AI to a vehicle that allows someone to travel faster without eliminating the need to understand where they are going.
“AI is amazing. It’s a tool that is like a car. It makes you move faster, but we still need to know what is rational and understand what AI is doing,” Fernandes said. “We may not be coding as much anymore but we need to know how to read code and assess if that code is doing the right job.”
Tim Beissinger, co-founder and chief technology officer for Heritable Agriculture, made a similar comparison using long division.
Beissinger recalled learning long division as a child and later teaching students how to code in the R programming language while working as a professor. As technology changes how those tasks are performed, he said understanding the principles behind them remains important.
“When I was in elementary school I learned long division,” Beissinger said. “My mom was a math education professor, and she thought it was crazy that some schools weren’t even teaching long division anymore … but you really don’t need it. When I was a professor, I taught how to code in R, and maybe that’s like long division now, but you need to understand long division. You need to understand how to ask a scientific question.”
That ability to ask the right question emerged as a recurring theme during the symposium.
Panelists said students entering an AI-driven workplace will need more than the ability to use the technology. They will need scientific reasoning and problem-solving skills and the ability to critically evaluate information produced by AI rather than automatically accepting its conclusions.
James Schnable, a professor and Nebraska Corn Checkoff Presidential Chair at the University of Nebraska-Lincoln, said AI has become increasingly capable in many areas but has not demonstrated the same improvement in determining which questions are worth asking.
“It’s figuring out what is the thing people actually need,” Schnable said. “I think in the AI world, this is called taste as opposed to ability. And I have seen, if anything, the taste of AI — what questions it asks if you give it freedom. They’re less aligned with what we actually need. It’s actually getting worse.”
Determining whether AI-generated results make sense will also remain an important human responsibility, according to panelists.
Dongyi Wang, an assistant professor of biological and agricultural engineering with the Division of Agriculture, said the sheer amount of information AI can produce creates another challenge.
“Our graduate students can produce a review paper in less than two weeks, but as a human it takes me a month to read hundreds of pages and carefully provide feedback because that’s still very important,” Wang said. “I would predict in the next five years, everyone will be buried in all kinds of information, not just text, but images and videos.”
The challenge, Wang said, will be determining how to use that expanding volume of information and separate what is useful from what is not.
Access to data presents another issue.
Some potentially valuable information remains inaccessible to AI because of how it is formatted or because it is privately held. Alexander Bucksch, an associate professor of plant science at the University of Arizona, said the scientific community should consider how research data is shared.
“In our time, we have a responsibility to start this discussion of ‘how does everybody have an equal transparency in terms of data, not only on Facebook, but also with research data,” Bucksch said. “Why can you hold back 20 years of data that could already revolutionize the world?”
Preparing students for an AI-driven workforce
The changing role of AI also raises questions about how colleges and universities should educate the next generation of scientists and agricultural professionals.
Panelists discussed moving some of the emphasis away from simply teaching programming syntax and toward broader skills such as software design, identifying problems and learning how to effectively work alongside AI.
Students will also need experience with the technologies they are likely to encounter after graduation, panelists said, while universities continue to emphasize academic integrity and independent thought.
At the conclusion of the roundtable, participants were asked to summarize AI in a single word. Their responses — including “trust,” “amazing,” “fun,” “transformative” and “scale” — reflected both the opportunities and questions surrounding the technology.
The discussion was moderated by Leonardo Bastos, an assistant professor of integrative precision agriculture at the University of Georgia and co-host of The Crop Science Podcast Show, along with Samuel B. Fernandes, an assistant professor of agricultural statistics and quantitative genetics with the Arkansas Agricultural Experiment Station and organizer of the symposium.
More information about agricultural and food research conducted in Arkansas is available through the Arkansas Agricultural Experiment Station.
John Lovett is with the University of Arkansas Division of Agriculture.