Winning contest entry shows AI’s agricultural potential

Source Domain: news.unl.edu

Nipuna Chamara, research assistant professor in biological systems engineering at the University of Nebraska–Lincoln, won a category of the 2025 Testing Ag Performance Solutions competition using artificial intelligence to make decisions for him.

The takeaway? AI can be an invaluable agricultural tool when paired with farmers’ experience and know-how.

“If a person like me, who’s not a farmer, can use AI to win a competition like this, imagine what a seasoned farmer, with decades of experience and knowledge, could do with this tool,” Chamara said.

The TAPS program is a university-led, real-world competition where participants manage actual plots of corn and soybeans for an entire growing season. The three judging categories are highest yield, highest profitability and highest input use efficiency. Participants make many of the same decisions for their TAPS plot that they make at home — such as seed selection, irrigation, pest control and grain marketing — but in a low-risk environment where they can experiment without harming or diminishing crops on their working farms.

Each team is given a plot or plots of land on which to grow their crops. Extension educators provide the teams with data, such as moisture levels and soil health, after which teams decide how much and when to water, fertilize or make other such choices. The results are tallied in September, and the winners are announced in January the following year.

In 2025, the TAPS fields were located at the Research, Extension and Education Centers in North Platte and Mead, Nebraska.

“The average grower has about 40 growing seasons to improve their operation,” said Chris Proctor, a Nebraska Extension educator who helps manage the contest. “As soon as the seed goes in the ground, they’re kind of locked in for that year. Within TAPS, last year we got 116 teams competing, so that’s 116 growing seasons’ worth of decisions all in one. So, in some ways it accelerates the learning iterations that…

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