Detection of Citrus Psyllid Based on Improved YOLOX Model  

作　　者：Haiman WANG Ting YU Ganjun YI Deqiu LIN Min LUO 

机构地区：[1]College of Information Science and Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China [2]Agro-biological Gene Research Center,Guangdong Academy of Agricultural Sciences/Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization,Guangzhou 510640,China [3]Guangdong Academy of Agricultural Sciences,Guangzhou 510640,China [4]Lianjiang Association of Economic and Social Development Research,Lianjiang 524400,China

出　　处：《Plant Diseases and Pests》2023年第1期17-21,共5页植物病虫害研究（英文版）

基　　金：Supported by Research and Development Program in Key Areas of Guangdong Province(2020B0202090005);Lianjiang Think Tank Enterprise Project"Demonstration of Intelligent Monitoring and Ecological Prevention and Control Technology of Red Orange Yellow-shoot Disease and Psyllid in Lianjiang"。

摘　　要：[Objectives]The paper was to explore a faster and more accurate detection method for citrus psyllid to prevent and control yellow-shoot disease and inhibit its transmission.[Methods]We used an improved YOLOX based edge detection method for psyllid,added Convolutional Block Attention Module(CBAM)to the backbone network,and further extracted important features in the channel and space dimensions.The Cross Entropy Loss in the object loss was changed to Focal Loss to further reduce the missed detection rate.[Results]The algorithm described in the study fitted in with the detection platform of psyllid.The data set of psyllid was taken in Lianjiang Orange Garden,Zhanjiang City,Guangdong Province,deeply adapted to the actual needs of agricultural and rural development.Based on YOLOX model,the backbone network and loss function were improved to achieve a more excellent detection method of citrus psyllid.The AP value of 85.66%was obtained on the data set of citrus psyllid,which was 2.70%higher than that of the original model,and the detection accuracies were 8.61%,4.32%and 3.62%higher than that of YOLOv3,YOLOv4-Tiny and YOLOv5-s,respectively,which had been greatly improved.[Conclusions]The improved YOLOX model can better identify citrus psyllid,and the accuracy rate has been improved,laying a foundation for the subsequent real-time detection platform.

关 键 词：CITRUS Improved YOLOX model Prevention and control of psyllid Artificial intelligence Object detection 

分 类 号：S436.66[农业科学—农业昆虫与害虫防治]