AI down on the farm

Authors: Sudduth, Kenneth - Ken; Woodward-Greene, Jennifer; Penning, Bryan; Locke, Martin; Rivers, Adam; Veum, Kristen

Submitted to: IEEE IT Professional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2020
Publication Date: 5/21/2020
Citation: Sudduth, K.A., Woodward Greene, M.J., Penning, B., Locke, M.A., Rivers, A.R., Veum, K.S. 2020. AI down on the farm. IEEE IT Professional. 22(3):22-26. https://doi.org/10.1109/MITP.2020.2986104.
DOI: https://doi.org/10.1109/MITP.2020.2986104

Interpretive Summary: N/A

Technical Abstract: Agriculture has become an information-intensive industry. In the production of crops and animals, precision agriculture approaches have resulted in the collection of spatially and temporally dense datasets by farmers and agricultural researchers. These big datasets, often characterized by extensive nonlinearities and interactions, are often best analyzed using machine learning (ML) or other artificial intelligence (AI) approaches. In this article, we review several case studies where ML has been used to model aspects of agricultural production systems and provide information useful for farm-level management decisions. These studies include modeling animal feeding behavior as a predictor of stress or disease, providing information important for developing precise and efficient irrigation systems, and enhancing tools used to recommend optimum levels of nitrogen fertilization for corn. Taken together, these examples represent the current abilities and future potential for AI applications in agricultural production systems.