A multi-modal transformer trained on Landsat, climate, ET, and soil data reaches R²=0.76 for county-level yield forecasts of 70+ California crops on held-out test years 2021-2022.
A gnn-rnn approach for harnessing geospa- tial and temporal information: application to crop yield pre- diction
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California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops
A multi-modal transformer trained on Landsat, climate, ET, and soil data reaches R²=0.76 for county-level yield forecasts of 70+ California crops on held-out test years 2021-2022.