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This study presents a novel deep learning model, CNN-GRU, which combines convolutional neural networks and gated recurrent units to improve winter wheat yield estimation in the Guanzhong Plain, China, using remotely sensed data. The model demonstrated higher accuracy (R2 = 0.64) compared to traditional methods, effectively capturing the cumulative effects of multi-variables on yield over time. The research highlights the importance of specific growth stages for timely yield predictions, showcasing the poten

Author

ak.abid241

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EN