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be tested for a variety of highly relevant problems in agriculture, like crop type classification, crop yield forecasting, field boundary delineation, crop disease, and crop failure detection. The Ph.D
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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