Research on the Method of Crop Pest and Disease Recognition Based on the Improved YOLOv7-U-Net Combined Network

This paper proposes an improved YOLOv7-U-Net combined network for crop pest and disease recognition, aiming to address the issue of insufficient accuracy in existing methods. For the YOLOv7 network, a self-attention mechanism is integrated into the SPPCSPC module to dynamically adjust channel weights and suppress redundant information while optimizing the PAFPN structure to enhance cross-scale feature fusion and improve small-object detection capabilities. For the U-Net network, the CBAM attention module is added before decoder skip connections, and depth-separable convolutions replace traditional kernels to strengthen feature fusion and detail attention. Experimental results show the improved algorithm achieves 97.49% detection accuracy, with mean average precision (mAP) reaching 96.91% and detection speed increasing to 90.41 FPS. The loss function of the improved U-Net network decreases towards 0 with training iterations, validating its effectiveness. The study shows that the improved YOLOv7-U-Net combined network provides a more effective solution for crop pest and disease detection.