摘要
【目的】黄龙病被称为柑橘的“癌症”,是一种毁灭性病害,而木虱是黄龙病传播的主要媒介,对木虱的监测和精准消杀是防控黄龙病及抑制其传播的一种有效途径。【方法】传统方式消灭木虱主要是靠人工喷洒药物,人力成本高但防控效果并不理想。采用基于改进YOLOX的木虱边缘检测方法,在主干网络加入卷积注意力模块CBAM(Convolutional block attention module),在通道和空间两个维度对重要特征进行进一步提取;将目标损失中的交叉熵损失改为使用Focal Loss,进一步降低漏检率。【结果】本研究设计的算法契合木虱检测平台,木虱数据集拍摄于广东省湛江市廉江红橙园,深度适应农业农村实际发展需要,基于YOLOX模型对骨干网络和损失函数做出改进实现了更加优秀的柑橘木虱检测方法,在柑橘木虱数据集上获得85.66%的AP值,比原始模型提升2.70个百分点,检测精度比YOLOv3、YOLOv4-Tiny、YOLOv5-s模型分别高8.61、4.23、3.62个百分点,识别准确率大幅提升。【结论】改进的YOLOX模型可以更好地识别柑橘木虱,准确率得到提升,为后续实时检测平台打下了基础。
【Objective】Yellow-shoot disease,known as the cancer of citrus,is a devastating disease,and psyllid is the main vector of yellow-shoot disease transmission,therefore,monitoring and precise disinfection and sterilization of psyllid is an effective way to prevent and control yellow-shoot disease and inhibit its transmission.【Method】The traditional way to eliminate the psyllid was mainly to spray drugs manually,and the control effect was not ideal due to high labor costs.In the study,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 target loss was changed to Focal Loss to further reduce the missed detection rate.【Result】The results showed that 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.It is 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.85.66%of the AP value was obtained on the data set of citrus psyllid,which was 2.70 percentage points higher than that of the original model,and the detection accuracy was 8.61,4.32 and 3.62 percentage points higher than that of YOLOv3,YOLOv4-Tiny and YOLOv5-s respectively,which has been greatly improved.【Conclusion】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.
作者
王海漫
俞婷
肖明明
杨嘉诚
陈富荣
易干军
林德球
罗敏
WANG Haiman;YU Ting;XIAO Mingming;YANG Jiacheng;CHEN Furong;YI Ganjun;LIN Deqiu;LUO Min(Agro-biological Gene Research Center,Guangdong Academy of Agricultural Sciences/Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization,Guangzhou 510640,China;College of Information Science and Technology,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China;College of Information and Communication Engineering,Guangzhou Maritime University,Guangzhou 510725,China;Guangdong Academy of Agricultural Sciences,Guangzhou 510640,China;Lianjiang Association of Economic and Social Development Research,Lianjiang 524400,China)
出处
《广东农业科学》
CAS
2022年第11期43-49,共7页
Guangdong Agricultural Sciences
基金
广东省重点领域研发计划项目(2020B0202090005)
廉江智库企业项目(廉江红橙黄龙病与木虱智慧监控及生态防控技术应用示范)。
关键词
柑橘
改进YOLOX模型
木虱防控
人工智能
目标检测
citrus
improved YOLOX model
prevention and control of psyllid
artificial intelligence
object detection
