基于YOLOv3深度学习算法的桑椹菌核病严重度检测方法研究与应用  

Research and application of detection method of mulberry fruit sclerotiniose disease severity based on YOLOv3 deep learning algorithm

作　　者：朱志贤 邱盼 张成[1] 董朝霞 张凤[1] 胡兴明[1] 于翠[1] ZHU Zhi-xian;QIU Pan;ZHANG Cheng;DONG Zhao-xia;ZHANG Feng;HU Xing-ming;YU Cui(Institute of Economic Crops,Hubei Academy of Agricultural Sciences,Wuhan 430064,China;Feejoy Technology(Shanghai)Co.,Ltd.,Shanghai 201506,China)

机构地区：[1]湖北省农业科学院经济作物研究所,武汉430064 [2]飞卓科技(上海)股份有限公司,上海201506

出　　处：《湖北农业科学》2024年第12期191-198,共8页Hubei Agricultural Sciences

基　　金：国家重点研发计划支持项目(2020YFD1000700);湖北省农业科技成果转化资金项目(2024EBA009);国家现代农业产业技术体系建设专项(CARS-18-SYZ10)。

摘　　要：通过对5种不同发病级别的1万张桑椹果实图像进行训练,基于YOLOv3深度学习算法并结合迁移学习法,获得桑椹菌核病严重度目标检测模型。为了验证该模型的鲁棒性,与同样采用迁移学习的EfficientDet、Faster R-CNN和YOLOv4原始模型进行了对比。结果表明,YOLOv3模型对健康果实和菌核病果实检测的平均精确率均值为0.79,比其他模型提高6.76%~54.90%,其对不同发病级别菌核病果实检测的平均精确率比其他模型提高7.04%~80.95%,查准率和查全率为最优或者次优。采用Flask+Vue技术构建的检测识别系统可在1 s内获取病害严重度、果实大小、置信度信息,也能实现对视频的动态识别,为桑椹种植中自动化病害监测和快速高效精准施药提供了可靠的软件处理平台。A target detection model for mulberry fruit sclerotiniose disease severity was constructed based on YOLOv3 deep learning algorithm combined with transfer learning by training on 10000 images of mulberry fruit with five different disease severity levels.To verify the robustness of the YOLOv3 model,comparative experiments were conducted with the EfficientDet,Faster R-CNN and YO⁃LOv4 that also used transfer learning.The results showed that the average precision rate of the YOLOv3 model could reach 0.79 for de⁃tecting healthy fruits and sclerotinia fruit,which was 6.76%~54.90%higher than that of the other models.The average precision rate of the YOLOv3 model for detecting disease severity levels of sclerotinia fruit was 7.04%~80.95%higher than that of the other models.The detection precision rate and recall rate of the YOLOv3 model were optimal or sub-optimal.The detection and recognition system con⁃structed by Flask+Vue technology could obtain disease severity,fruit size and confidence information within 1 s,and could also real⁃ize dynamic recognition of video.This system could provide a reliable software processing platform for automated disease monitoring and fast,efficient,and precise fungicide application during mulberry cultivation.

关 键 词：桑椹菌核病 深度学习算法 迁移学习法 YOLOv3 病害严重度检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S224[自动化与计算机技术—计算机科学与技术] F327[农业科学—农业机械化工程]