Predicting intra-field yield variations for winter wheat using remote sensing and Graph Attention Networks
Accurate prediction of spatial yield variations within individual fields is crucial for precision agriculture, as it enables optimized resource allocation and targeted crop management. In this study, we propose a novel framework that leverages remote sensing data and Graph Attention Networks (GATv2) to predict fine-scale yield variations for winter wheat at a high resolution (10 m × 10 m). The obj
