SAFF-YOLO-BASED LIGHTWEIGHT DETECTION METHOD FOR THE DIAMONDBACK MOTH

The diamondback moth (Plutella xylostella) is a destructive pest that severely compromises Chinese cabbage production. Infestations caused by this pest significantly reduce both yield and quality, making efficient and accurate detection crucial for cultivation management. To address the challenges of detecting small targets and extracting phenotypic features in complex environments, this study proposes SAFF-YOLO—a YOLO11-based pest detection algorithm specifically designed for diamondback moths in Chinese cabbage fields. First, the loss function was refined to enhance the model's learning capacity for pest samples, optimizing it for precision-driven bounding box regression. Second, Alterable Kernel Convolution (AKConv) was incorporated into the backbone network, strengthening feature extraction capabilities while reducing model parameters. Third, Single-Head Self-Attention (SHSA) was integrated into the C2PSA (Channel and Position Spatial Attention) module, enhancing the backbone network's feature processing efficacy. Fourth, the neck network employed Frequency-aware Feature Fusion (FreqFusion) as the upsampling operator, specifically designed for precise localization of densely distributed targets. Finally, the Feature Auxiliary Fusion Single-Stage Head (FASFFHead) detection module was implemented to boost multi-scale target detection adaptability. Experimental results demonstrate that SAFF-YOLO achieved detection metrics of 90.7% precision, 89.4% recall, and 92.4% mAP50 for diamondback moths in Chinese cabbage, representing improvements of 7.4%, 8.0%, and 8.4% respectively over YOLO11. With only 7.3 million parameters and computational cost of 12.8 GFLOPs, this corresponds to 60.1% and 40.7% reductions compared to the baseline model. These results confirm an optimal balance between model lightweighting and high detection accuracy. Under complex field conditions characterized by small and densely distributed targets, severe background interference, and intense illumination, SAFF-YOLO consistently demonstrates robust detection capabilities, effectively reducing both false negative and false positive rates while maintaining high operational robustness. This research provides a practical solution for real-time diamondback moth detection in field-grown Chinese cabbage.