Arkansas Scientists Employ Machine Learning to Manage Corn Crops More Efficiently

Professors Jia Di, left, and Trent Roberts inspect a prototype corn sensor set up in a test plot at the Arkansas Agricultural Research and Extension Center. FAYETTEVILLE, Ark. – A team of researchers from the University of Arkansas System Division of Agriculture and the University of Arkansas College of Engineering is designing tiny sensors that can be placed in corn stalks to monitor water, nitrogen and potassium needs in real time. The data collected from those sensors -- matched with geographic, weather and other environmental data -- will feed machine learning software to develop models that will be able to predict when a crop will need those inputs before the conditions exist. Those predictive models can help corn growers give their crops exactly the water and nutrients they need, before they experience stress, to achieve the best possible yields without wasting resources. The collaborative research by the division's Arkansas Agricultural Experiment Station and the university's College of Engineering is supported by the Chancellor's Discovery, Creativity, Innovation and Collaboration Fund.