华中农业大学学报2026，Vol.45Issue(3)：98-114,17.DOI:10.13300/j.cnki.hnlkxb.2026.03.009
基于剪枝和知识蒸馏的YOLOv8轻量化苹果叶片病害检测方法
A pruning and knowledge distillation-based YOLOv8 lightweight method for detecting leaf diseases in apple
摘要
Abstract
A lightweight,real-time model of detecting leaf diseases in apple,named as SMPD-YO-LO,was proposed based on YOLOv8n to solve the problems in existing methods of detecting leaf diseases in apple in terms of accuracy,real-time performance,and robustness under complex and noisy environ-ments.The spatial pyramid pooling fast cross stage partial CSP(SPPFCSPC)module was embedded into the backbone network to enhance the capability of feature fusion.The minimum point distance-IoU(MPD-IoU)was introduced as the bounding box regression loss function to improve the accuracy and convergence speed of the model.The model volume and floating point operations were further reduced via layer adaptive magnitude-based pruning(LAMP).A channel-wise knowledge distillation(CWD)strategy was used to boost detection performance.The results showed that the improved SMPD-YOLO model had a mean aver-age precision(mAP@0.5)of 90.20%and a frame rate of 133.3 frames per second(FPS),while the weight and FLOPs of model was 5.0 MB and 7.3×109 s-1,respectively.The improved model maintained excellent robustness under complex and noisy environments including strong illumination,weak light,and blurred im-ages.It is indicated that the SMPD-YOLO model combines high accuracy,lightweight design,and real-time performance,enabling the efficient detection of leaf diseases on resource-constrained equipment.关键词
苹果/叶片病害检测/YOLOv8/轻量化/自适应幅度剪枝/知识蒸馏Key words
apple/detection of leaf diseases/YOLOv8/lightweight/adaptive magnitude-based prun-ing/knowledge distillation分类
信息技术与安全科学引用本文复制引用
张帅平,时雷,郑光,王慧新,尹飞..基于剪枝和知识蒸馏的YOLOv8轻量化苹果叶片病害检测方法[J].华中农业大学学报,2026,45(3):98-114,17.基金项目
河南省科技攻关项目(242102521027) （242102521027）
河南省科技研发计划联合基金项目(222301420113) （222301420113）
