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Abstract: From Ground Tests to Aerial Imagery: Redefining Irrigation Distribution Uniformity Assessments for California Almonds using Machine Learning (AGU25)

From Ground Tests to Aerial Imagery: Redefining Irrigation Distribution Uniformity Assessments for California Almonds using Machine Learning

Jaya Suneja, University of Wisconsin Madison, Madison, WI, United States, Erica Edwards, University of California Davis, Department of Land, Air, and Water Resources, Davis, CA, United States, Andrew Gal, University of California, Davis, Department of Land, Air, and Water Resources, Davis, United States, Kyle Knipper, U. S. Department of Agriculture, Agricultural Research Service, Sustainable Agricultural Water Systems Research Unit, Davis, United States, Micah Levinson, United States Department of Agriculture, Agricultural Research Service, Sustainable Agricultural Water Systems Unit, Davis, United States, Conor Higgins, Yolo County Resource Conservation District, Woodland, CA, United States and Mallika Nocco, University of Wisconsin Madison, Dept. of Biological Systems Engineering, Madison, United States
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