A U-Net segmentation model trained on 64-band AlphaEarth embedding chips achieves 99.19% pixel accuracy and 99.04% F1 on an independent test set for distinguishing tomato from non-tomato fields in California.
, author M a lina s , A
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Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis
A U-Net segmentation model trained on 64-band AlphaEarth embedding chips achieves 99.19% pixel accuracy and 99.04% F1 on an independent test set for distinguishing tomato from non-tomato fields in California.