This $250,000 National Science Foundation project grant supports the development of spatiotemporally transferable machine learning algorithms for enabling cross-country and cross-hemisphere in-season crop mapping. Funded under the Computer and Information Science and Engineering program, principal investigator George Mason University will design algorithms trained with U.S. agricultural data that can be applied to satellite imagery of foreign countries to generate in-season crop maps without on-the-ground verification data. If successful, these algorithms could significantly enhance the competitiveness of U.S. agriculture by facilitating early crop forecasts globally. They may also increase world food security and potentially provide billions of dollars in economic benefits to U.S. farmers through more informed international commodity trade. The project period is September 1, 2022 through August 31, 2024.
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