中国森林病虫2026，Vol.45Issue(1)：24-33,10.DOI:10.19688/j.cnki.issn1671-0886.20250044
基于无人机影像和改进YOLOv11算法的松材线虫病变色疫木检测
Detection of discolored trees infected by pine wilt disease based on improved YOLOv11 algorithm and UAV imagery
摘要
Abstract
To improve the detection accuracy of pine wilt disease based on deep learning,an improved YOLOv11 algorithm was constructed by combining wavelet convolution(WTConv)with triplet attention mechanism(triplet attention)based on YOLOv11 algorithm.Based on UAV visible light remote sensing image,the algorithm was applied to the detection of pine wilt disease in Jinyun county,Zhejiang province.The detection effects of YOLOv8,YOLOv11 and improved YOLOv11 algorithm on infected and dead trees caused by pine wilt disease were compared.The results showed that the improved YOLOv11 algorithm had better performance.The average detection accuracy was 97.7%,the precision was 97.4%,and the recall was 95.4%,which were better than YOLOv8 and YOLOv11 algorithms.In the untrained area,the F1 score of pine wilt disease infected tree was 94.4%,and the improved YOLOv11 algorithm model could still accurately identify the target in the untrained area.The research results provided a more accurate detection algorithm for pine wilt disease,and provided more accurate tool support for the location of pine wilt disease.关键词
松材线虫病/变色疫木/YOLO算法/无人机影像/目标检测Key words
pine wilt disease/discolored epidemic tree/YOLO algorithm/UAV image/target detection分类
农业科技引用本文复制引用
陈筱涵,陈广生,周均瑞,陈利杰,刘闯,陈潇扬..基于无人机影像和改进YOLOv11算法的松材线虫病变色疫木检测[J].中国森林病虫,2026,45(1):24-33,10.基金项目
政府间国际科技创新合作重点研发计划项目"典型森林生态系统韧性调控机制与适应性管理"(2023YFE0105100) （2023YFE0105100）
