安庆师范大学学报（自然科学版）2026，Vol.32Issue(2)：57-62,6.DOI:10.13757/j.cnki.cn34-1328/n.2026.02.008
基于优化YOLOv5s的苹果叶片病害检测
Apple Leaf Disease Detection Based on Optimized YOLOv5s
摘要
Abstract
Plant leaf pest and disease detection can identify the occurrence of pests and diseases at an early stage,reduce crop losses,and ensure the stable progress agricultural production.The manual visual observation method has problems such as low detection efficiency and longtime consumption.This paper proposes an improved YOLOv5s algorithm for the intelli-gent detection of pests and diseases in apple leaves.First,the Focal-EIOU(focal efficient intersection over union,Focal-EIOU)loss function is introduced to replace the CIOU(complete intersection over union,CIOU)loss function in the original network model,thereby optimizing the bounding box convergence effect,reducing missed detections,and improving the mod-el recall rate.At the same time,the activation function is improved by using the Mish activation function to replace the SiLU activation function in the original network model to alleviate the gradient vanishing problem in deep networks.Finally,an ap-ple leaf pest and disease detection experiment is conducted on the public dataset Plant village to verify the functionality of the proposed algorithm.The experimental results show that the recall rate and average precision of the improved YOLOv5s(you only look once v5 small,YOLOv5s)model reached 89.3%and 93.6 respectively.Compared with the original YOLOv5s mod-el,the recall rate and average precision were increased by 1.2%and 1.5%,respectively.The improved v5s model improves the detection accuracy of apple leaf disease targets while ensuring real-time detection,which is of great significance for the pre-vention of leaf pests and diseases.关键词
深度学习/目标检测/YOLOv5s/EIOU/Mish激活函数Key words
deep learning/object detection/YOLOv5s/EIOU/Mish activation function分类
信息技术与安全科学引用本文复制引用
石冰,周涵,王远志..基于优化YOLOv5s的苹果叶片病害检测[J].安庆师范大学学报（自然科学版）,2026,32(2):57-62,6.基金项目
国家重点研发计划项目(SQ2020YFF0402315) （SQ2020YFF0402315）
