Symmetry-Aware SXA-YOLO: Enhancing Tomato Leaf Disease Recognition with Bidirectional Feature Fusion and Task Decoupling

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy.