中国农业气象2026，Vol.47Issue(2)：202-215,14.DOI:10.3969/j.issn.1000-6362.2026.02.004
基于改进的YOLOv11检测苹果树叶片黑腐病
Apple Leaf Black Rot Detection Based on Improved YOLOv11 Model
摘要
Abstract
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.关键词
苹果黑腐病/YOLOv11/目标检测/深度可分离卷积Key words
Apple black rot/YOLOv11/Object detection/Depthwise separable convolution引用本文复制引用
张莉,王裕灿..基于改进的YOLOv11检测苹果树叶片黑腐病[J].中国农业气象,2026,47(2):202-215,14.基金项目
中国气象局河南省农业气象保障与应用技术重点实验室应用技术研究基金项目(KF202546) （KF202546）
