江苏农业学报2025，Vol.41Issue(3)：537-548,12.DOI:10.3969/j.issn.1000-4440.2025.03.013
基于改进YOLOv8的水稻病害检测算法
Rice disease detection algorithm based on improved YOLOv8
摘要
Abstract
To improve the detection performance of rice diseases,this study proposed an improved YOLOv8n detec-tion algorithm.Firstly,the Slim-Neck structure was introduced into the neck network.Ghost shuffle convolution(GSConv)was adopted to reduce the computational cost.At the same time,the cross-stage partial network module based on the one-shot aggregation method(VoVGSCSP)was combined to simplify the calculation process and network structure.The similar-ity-aware activation module(SimAM)attention mechanism was utilized to enhance the model's sensitivity to subtle color changes of disease spots.Finally,the adaptive feature pyramid network(AFPN)module was combined with the head struc-ture.Through the feature fusion of non-adjacent layers,the color,shape,and texture of the diseased areas were accurately captured.The experimental results showed that the precision,recall,and mean average precision at an intersection over u-nion threshold of 0.50(mAP50)of the improved model YOLOv8n-SMAF reached 85.1%,79.7%,and 83.7%respectively.Compared with the original model YOLOv8n,the precision,recall,and mAP50 of the improved model YOLOv8n-SMAF in-creased by 3.8 percentage points,4.5 percentage points,and 2.7 percentage points respectively.Compared with other mainstream models such as SSD,YOLOv7-tiny and YOLOv10n,the YOLOv8n-SMAF model had higher detec-tion accuracy,especially showing advantages in detection tasks in complex scenarios.The improved model in this study provides technical support for the early warning and precise prevention and control of rice diseases.关键词
水稻病害/目标检测/YOLOv8/深度学习/图像处理Key words
rice diseases/target detection/YOLOv8/deep learning/image processing分类
信息技术与安全科学引用本文复制引用
靳新宇,于复兴,索依娜,宋小明..基于改进YOLOv8的水稻病害检测算法[J].江苏农业学报,2025,41(3):537-548,12.基金项目
国家自然科学基金项目(32172583) （32172583）
