基于改进的YOLOv11检测苹果树叶片黑腐病OA

Apple leaf black rot is a common and destructive disease that severely affects apple quality and yield.To address the poor sensitivity to small targets,background clutter,and low efficiency of traditional identification methods,this study proposed an improved YOLOv11-based detector.A C3K2 module was introduced into the backbone to enhance multi-scale feature modeling;a C2PSA attention module was appended after the SPPF block;and the detection head adopted depthwise separable convolutions together with Distribution focal loss(DFL)and CIoU loss.The improved model achieved an mAP of 99.5％,a recall of 99.7％,and an F1-score of 99.6％,outperforming YOLOv8 by 3.2 percentage points and reaching 48 frames·s-1.Ablation experiments showed that combining C3K2,C2PSA and depthwise separable convolutions raised mAP from 93.1％to 95.2％.The proposed method ensures high-precision detection of small black rot lesions on apple leaves while markedly improving real-time performance,and has strong practicality and deployment value.