ENRTF ID: 243-F Project Title: Predicting Agriculture’s Outcomes with Sensors and Machine Learning Category: F. Methods to Protect, Restore, and Enhance Land, Water, and Habitat Summary: We will use low-cost sensors on long-term cropping system experiments to develop advanced machine learning algorithms that will predict yield and water quality outcomes across the southern half of Minnesota Total Project Budget: $ 887,005 Proposed Project Time Period for the Funding Requested: June 30, 2024 (4 yrs) Name: Philip Pardey Sponsoring Organization: U of MN Job Title: Professor Department: GEMS Agroinformatics Initiative Address: 248B Ruttan Hall, 1994 Buford Avenue St. Paul MN 55108 Telephone Number: (507) 381 6993 Email ppardey@umn.edu Web Address: www.agroinformatics.org Location: Region: Central, Metro, Southwest, Southeast County Name: Blue Earth, Brown, Carver, Chippewa, Cottonwood, Dakota, Dodge, Faribault, Fillmore, Freeborn, Goodhue, Houston, Jackson, Kandiyohi, Lac qui Parle, Le Sueur, Lincoln, Lyon, Martin, McLeod, Meeker, Mower, Murray, Nicollet, Nobles, Olmsted, Pipestone, Redwood Alternate Text for Visual: Visual illustrates field sites at Waseca and Lamberton. Shows how small each sensor node is and how the nodes will be deployed across each field site. Illustrates how we combine sensor data with machine learning to map water quality and yields.