Artificial intelligence (AI) and machine learning (ML) are two hot topics in the agriculture technology sector in the U.S. Companies are vying for a share of this growing sector of the marketplace. It’s a big part of the next evolutionary step in crop production, much like when John Deere introduced his first steel moldboard plow in 1837.

SwissCognitive Guest Blogger: Jeff Caldwell, Content Marketing Manager, Lessing-Flynn

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningThe agricultural marketplace — and where farmers will be willing to spend their money on AI and ML technology — will ultimately decide the winners and losers. But there are things agriculture companies can do to position themselves to take the lead, and it all starts with the kind of purpose that drove John Deere to become one of the most well-known names in a global industry for centuries.

But first, what’s AI got to do with agriculture? For crop producers, data has changed how they do business. It’s been both a blessing and a curse since precision ag technology entered the industry around 25 years ago. Row crop farmers can today, for example, monitor their crops and collect data with precision that wasn’t even remotely possible just a few years ago. But as data-gathering capabilities have grown, data utilization hasn’t exactly kept up. In other words, farmers were running out of ways to make use of their increasing crop data portfolios. Until AI.

Yes, building an effective AI algorithm for something like a crop weed control strategy requires a lot of data — way more than those to which some of the most tech-savvy farmers today have become accustomed. But increasingly, companies like Farmwave, one of the industry leaders in the agricultural AI space in the U.S. right now, have taken a different tact in developing algorithms and subsequent machine learning based not on the algorithm itself, but its ultimate function. Farmwave founder and president Craig Ganssle talks about “purpose-driven AI,” something that’s entirely lacking among many attempted entrants into the agricultural space.

Craig and Farmwave have discovered a recipe for success in introducing AI to agriculture. It started out not just about delivering some kind of new technology to the farm. It was more than that; a purpose-driven application that didn’t deliver something new to the farm but solved an existing problem. It took years for that to materialize, but today, Farmwave has an AI algorithm that can help farmers overcome weed control and crop yield loss challenges, two things with which they’ve historically struggled.

Craig and his team started with the function. He started with existing challenges and essentially created a better mousetrap, one that continues to evolve and help farmers overcome those and soon new challenges in their crop fields. His has been a roadmap that was laid out to me years ago by an old boss: “Don’t create a solution looking for a problem.”

But that’s what’s challenging the development of AI and ML for agriculture today. Many companies are thinking the technology first, function second. In many sectors, that strategy works. Not in agriculture, though. Agriculture is a highly branded industry. Farmers are accustomed to being sold something — machinery, crop seed, fertilizer, chemical — at all times. They know a sales pitch when they see one, and if a company is promising a high-tech solution to a problem that may or may not already exist, it automatically fuels skepticism. Farmers know what works, what challenges they face and the basics of how to overcome them. If you’re promising earth-moving results that don’t align with your end-users’ realistic expectations, you’re not likely to get an audience with even the most technology-savvy farmers in the world.

For AI companies looking to enter agriculture, take a low-tech approach to the marketplace before you begin to apply your technology to industry solutions. When he introduced his first steel moldboard plow to farmers in 1837, John Deere wasn’t greeted with immediate success. It took him years of listening and work to show farmers that the equipment would help them become more productive. And when he introduced his first tractor in 1918, farmers were skeptical. How could this machine do better than the teams of horses or other yoked animals farmers used to pull their plows? Yet here we are today, the tractor a machine no farm is without.

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We’re at a similar juncture with AI in agriculture today. Farmers have existing challenges ahead of them, whether it’s something like weed control or harvest crop loss or the growing carbon farming marketplace. The successful agricultural AI companies will be those who listen first, acknowledge these challenges, then offer feasible, progressive solutions that can make farmers better at what they already do so well. And you won’t change the world in an instant; but like John Deere, you could offer solutions that not only meet initial needs but entirely change how crops are produced around the world today.

If you’re developing AI solutions for agriculture, take the time to step out into the countryside and visit with a farmer or two — or 100 — to get a feel for what they need and what you can do to create the next big purpose-driven success in what will ultimately be a big part of crop production in the near future. If you’d like to talk about how you can make that happen, get in touch. I’d love to talk.

About the Author:

Jeff Caldwell is a 20-year veteran of agricultural media and marketing in Iowa in the U.S. He’s worked in the ag technology space, with start-ups and large, established companies alike, since 2010 and has a vast network of colleagues and connections in various industry sectors, including artificial intelligence. When he’s not studying, researching or writing about the latest ag technology topic, he’s raising his two daughters, trying to enjoy the outdoors and thinking of the next big thing in ag-tech.