John Reid, CS, ABE and ECE research professor and executive director of the Center for Digital Agriculture, received the 2026 Cyrus Hall McCormick-Jerome Increase Case Gold Medal. The award, presented by the American Society of Agricultural and Biological Engineers (ASABE), honors exceptional meritorious engineering achievement in agriculture that has resulted in new concepts, products, processes or methods that advanced the development of agriculture.

Congratulations on the Cyrus Hall McCormick-Jerome Increase Case Gold Medal.

Thank you. As it turns out, my career has connected me in unexpected ways to both individuals whose names are associated with this award—Cyrus McCormick and Jerome Increase Case. The first connection is that I grew up about 10 miles from Raphine, Virginia, where McCormick developed and successfully demonstrated the mechanical reaper in 1831. He later moved to Chicago to build the company that eventually became part of International Harvester.

My second connection came through Case IH. While serving on the Illinois faculty from 1986 to 2000, I conducted research with Case IH on autonomous agricultural vehicles. In 1996, our team in Agricultural and Biological Engineering developed and field-tested some of the earliest autonomous tractors for production agriculture on the University of Illinois South Farms. Computer scientists, electrical engineers and agricultural engineers worked together to demonstrate how sensing, computing and machinery could be integrated into autonomous systems.

In 2001, I joined John Deere and had the opportunity to help commercialize many of the technologies that enabled the industry’s transition from automation to autonomy. Seeing ideas move from research laboratories into products used by farmers around the world was an especially rewarding part of my career.

Returning to Illinois in 2022 gave me an opportunity to apply lessons learned from industry to new challenges in autonomy, robotics and AI. What makes this recognition especially meaningful is that it reflects the contributions of many students, colleagues and industry partners who shared in this journey. No one advances a field like agricultural automation alone.

“What makes this recognition especially meaningful is that it reflects the contributions of many students, colleagues and industry partners who shared in this journey. No one advances a field like agricultural automation alone. ”

CS, ABE, ECE professor and director of the Center for Digital Agriculture John Reid

So you grew up in Virginia?

I grew up in the small town of Staunton, Virginia, and my grandparents were farmers. Like many kids growing up around agriculture, I spent plenty of time helping with field work and dairy operations. I often joke that I was valued more for my muscle than my brains back then, but those experiences taught me a great deal about the realities of farming. I enjoyed the hard work and the sense of accomplishment that came with it, but the engineer in me was always asking why certain jobs had to be so difficult and whether there might be a better way to do them. Looking back, those early experiences probably planted the seeds for my later interest in automation, productivity and using technology to help farmers.

What led you to Illinois?

I attended Virginia Tech for both my bachelor’s and master’s degrees in agricultural engineering. At the time, I envisioned earning my Ph.D. and eventually returning to Virginia Tech as a faculty member.  However, that changed after going to Texas A&M for doctoral studies. Living in Texas broadened my perspective and interest in new experiences.

When I finished my Ph.D., the opportunity to join the faculty at Illinois was a tremendous fit with my interests. The Midwest is at the center of U.S. production agriculture, and many of the engineering challenges that fascinated me were happening right here. Illinois offered the opportunity to work at the intersection of agriculture, engineering and technology, and it proved to be the right place to build my career.  I joined the faculty in 1986, left for John Deere in 2001, and after more than two decades in industry returned to Illinois in 2022. In many ways, Illinois has been home throughout most of my professional life.

What changes have you seen in agriculture and guidance during your career?

The digital transformation of agricultural equipment has been extraordinary. When I started working on automatic guidance systems, tractors were only beginning to incorporate electrohydraulic controls. Many of the components needed for computer-controlled steering simply did not exist, so researchers often had to develop custom solutions just to demonstrate what was possible.

One thing I learned early is that agriculture tends to adopt technologies after they have matured in other industries. In the 1980s, automotive manufacturers were rapidly integrating electronics and engine controls, and agriculture soon followed. During my PhD research, I integrated a PDP-11 mini-computer configured for machine vision sensing with a tractor to perform guidance experiments. The computer alone cost about $20,000 and was one of the most expensive parts of the entire system. Today, vastly more computing power exists in the embedded controllers found throughout modern agricultural equipment.

My research was often a little ahead of commercial reality, but that is a good place to be. In academia, our focus is often proving a technology can work. When I joined John Deere, I learned that commercialization requires answering a different set of questions: Can it be manufactured at scale? Is it reliable? Can it be serviced? Will farmers see enough value to adopt it? Those are very different challenges than building a successful research prototype.

What impressed me most was seeing how innovations move from the laboratory to products that improve productivity for farmers. Early in my career, I viewed automatic guidance primarily to reduce operator fatigue and the drudgery of repetitive tasks. Once the technology reached the field, we discovered additional benefits: improved productivity, more consistent performance and the ability to help operators of all experience levels achieve better results. That broader impact is what made the journey from research to commercialization so rewarding.

Photo Credit: Center for Digital Agriculture

How was this received?

In the 1980s, some thought automatic guidance was a terrible idea because, at the time, driving the tractor was considered one of the best jobs on the farm. Their reaction was often, “Why would you want to automate the fun part?” What we learned, however, was that automatic guidance wasn’t about replacing the operator. It was about improving performance and consistency. Once the technology reached farmers, they quickly recognized the value. It allowed operators with different skill levels to achieve similar results, which became increasingly important as farms grew larger and labor became more difficult to find. We also discovered productivity benefits. Machines could operate more efficiently, with less overlap and fewer errors, allowing farmers to cover more acres in the same amount of time. Those gains often translated directly into economic value for the operation.

Perhaps the biggest lesson for me was understanding the importance of the user experience. As a researcher, I was focused on proving that the technology worked. Once I moved into industry, I learned that success also depends on how easy the technology is to use, how reliable it is and whether customers can clearly see its value. That experience taught me that innovation isn’t complete until farmers can successfully adopt the technology.

So you saw a clear, necessary path forward?

I look at it differently today than I did when I started, but I can trace successful innovation to three things.

First, the technology has to be ready. In the 1980s, many of the technologies needed for autonomous agricultural systems simply weren’t mature enough. We were pushing the limits of computing, sensing and control systems, often using creative workarounds to make things function. Today, students have access to computing power, sensors and AI tools that we could only dream about. At the time, I thought solving the technical problem was the hard part. What I learned later is that technical feasibility is only the beginning.

Photo Credit: Triple Helix

Second, the technology must solve a meaningful problem for the intended user. Interestingly, nobody was asking for automatic guidance systems. Farmers weren’t looking for a way to steer a tractor automatically; they were looking for ways to be more productive. Once guidance systems became available, farmers quickly realized they could focus more attention on the implement and the quality of the field operation rather than on keeping the tractor precisely on a line. The technology solved a problem they hadn’t fully articulated.

The third element is economic viability. Farmers have to see a clear return on investment, and companies must have the ability to develop, manufacture, support and continuously improve the technology. A great invention that doesn’t create value for the customer or support a sustainable business model rarely succeeds.

Looking back, the real lesson is that innovation happens when those three elements come together: technology readiness, customer value and business viability. If any one of them is missing, adoption becomes very difficult.

Photo Credit: Center for Digital Agriculture

How did these things steer your career?

From the very beginning, I believed the future of agriculture would be defined not just by bigger machines, but by smarter machines. Throughout much of the twentieth century, agricultural productivity improved because machinery became larger, more powerful and more efficient. Those advances were tremendously important, but I saw an opportunity to improve precision and decision-making through sensing, computing and control systems.

My training as a controls engineer naturally led me in that direction. I became fascinated with the idea of giving machines the ability to perceive their environment, make decisions and perform tasks with greater accuracy than was possible through mechanical systems alone. That interest ultimately led me into computer vision, automation, robotics and, more recently, artificial intelligence.

One of the most remarkable changes I have witnessed is how agricultural equipment has evolved into true cyber-physical systems. Today’s machines are defined as much by software, electronics, sensing, connectivity and intelligence as they are by engines, hydraulics and steel. Capabilities that once existed only in research laboratories are now standard features on commercial equipment.

The pace of change continues to accelerate. When I started my career, agricultural companies were primarily mechanical engineering organizations. Today, they employ software developers, data scientists, AI specialists and robotics engineers alongside traditional engineering disciplines. It has been exciting to watch agriculture evolve from an industry focused primarily on mechanization to one increasingly driven by intelligence, automation and autonomy.

Photo Credit: Center for Digital Agriculture

What are you working on now?

When I returned to Illinois as a Research Professor, one of my motivations was that there were still important problems that industry had not fully solved. I also like to think that I’m a better professor today than I was 26 years ago because I can bring both academic and industry perspectives to the classroom and research lab.

As Executive Director of the Center for Digital Agriculture, our teams work to bring together researchers, students, industry partners and producers to accelerate the development and adoption of digital agriculture technologies.

A major focus of my research is perception system engineering for off-road autonomous systems, both for situational awareness and for sensing that supports job quality. While autonomy has advanced tremendously, there are still many challenges in making robotic systems reliable in complex agricultural environments. 

Additionally, I’m particularly interested in how automation and autonomy can benefit small- and medium-sized agricultural enterprises, not just the largest farming operations. One example is our work with horseradish growers in Illinois and Wisconsin. Horseradish production still relies on traditional mechanization and labor-intensive practices. It represents exactly the type of agricultural challenge where new automation approaches can have a meaningful impact but may not fit traditional commercial business models. Because this is a relatively small industry, it is difficult to justify dedicated commercial solutions. We are exploring how robotics, automation and AI can create affordable solutions for specialty crop producers that might otherwise be overlooked.

I have also become very active in the application of generative AI to agriculture. I view generative AI as the next step in a progression from mechanization to automation to autonomy and now to intelligent decision-support systems that can help farmers make better decisions. There is tremendous potential for AI systems to work directly with farmers, helping them interpret information, support decision-making and ultimately serve as intelligent agents that connect farm management systems, equipment and field operations. I see tremendous opportunities to improve productivity, decision-making and convenience enabled by AI.

Photo Credit: Center for Digital Agriculture

You've had an extensive and rewarding career.

Perhaps the most rewarding has been seeing students and young engineers take these ideas further than I ever imagined and become leaders in academia and industry around the world.

What continues to excite me is that many of the most important challenges are still ahead of us. Agriculture is entering an era where intelligent digital technologies can work together to improve productivity, sustainability and quality of life. It is an exciting time to be an engineer.