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This document presents an improved crop disease detection algorithm based on the YOLOv5 network, integrating a Squeeze-and-Excitation module and a lightweight up-sampling operator to enhance feature extraction and accuracy. The model achieved a detection accuracy of 90.0% and reduced parameter count and computational load by 23.9% and 31.2%, respectively, outperforming existing methods. This approach aims to provide real-time, automated identification of crop diseases, offering significant benefits for agri

Author

kartikambupe

Language

EN