APEIOU Integration for Enhanced YOLOV7: Achieving Efficient Plant Disease Detection

Abstract

Plant diseases can severely hamper plant growth and yield. Currently, these diseases often manifest diverse symptoms, characterized by small targets and high quantities. However, existing algorithms inadequately address these challenges. Therefore, this paper proposes improving plant disease detection by enhancing a YOLOV7-based model. Initially, we strengthen multi-scale feature fusion using the fourth prediction layer. Subsequently, we reduce model parameters and the computational load with the DW-ELAN structure, followed by optimizing the downsampling process using the improved SPD-MP module. Additionally, we enhance the Soft-SimAM attention mechanism to prioritize crucial feature components and suppress irrelevant information. To distinguish overlapping predicted and actual bounding box centroids, we propose the APEIOU loss function and refine the offset formula and grid matching strategy, significantly increasing positive samples. We train the improved model using transfer learning. The experimental results show significant enhancements: the mAP, F1 score, Recall, and Precision are 96.75%, 0.94, 89.69%, and 97.64%, respectively. Compared to the original YOLOV7, the improvements are 5.79%, 7.00%, 9.43%, and 3.30%. The enhanced model outperforms the original, enabling the more precise detection of plant diseases.