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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #371135
Research Project: Science and Technologies for the Sustainable Management of Western Rangeland Systems

Location: Range Management Research

Title: AI recommender system with ML for agricultural research

Author
item Peters, Debra
item Savoy, Heather
item RAMIREZ, GEOVANY - New Mexico State University
item HUANG, HAITAO - New Mexico State University

Submitted to: IEEE IT Professional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/26/2020
Publication Date: 5/1/2020
Citation: Peters, D.C., Savoy, H.M., Ramirez, G., Huang, H. 2020. AI recommender system with ML for agricultural research. IEEE IT Professional. 22:29-32.

Interpretive Summary: We describe an AI recommender system (RS) with machine learning to harness past user choices and large volumes of data, yet account for changes in weather and management decisions characteristic of agricultural systems. Our goal is to maximize the use of data relevant to solving agricultural problems and improve the efficiency of the scientific workforce while also improving the accuracy of estimates of the amount of food produced. Our example shows how the RS learns data analysis choices from user behavior for predicting agricultural production responses to rainfall and learns to identify classes of agroecosystem responses to alternative climate scenarios. We account for changes in relationships using spatial and temporal statistics. The RS provides a powerful approach to make use of the large amounts of data and scientific expertise in the agricultural enterprise to predict agroecosystem dynamics under changing environmental conditions.

Technical Abstract: We describe an AI recommender system (RS) with machine learning to harness past user choices and large volumes of data, yet account for changes in weather and management decisions characteristic of agricultural systems. Our goal is to maximize the use of data relevant to solving agricultural problems and improve the efficiency of the scientific workforce while also improving the accuracy of estimates of the amount of food produced. Our example shows how the RS learns data analysis choices from user behavior for predicting agricultural production responses to rainfall and learns to identify classes of agroecosystem responses to alternative climate scenarios. We account for changes in relationships using spatial and temporal statistics. The RS provides a powerful approach to make use of the large amounts of data and scientific expertise in the agricultural enterprise to predict agroecosystem dynamics under changing environmental conditions.