柑橘木虱YOLO v8-MC识别算法与虫情远程监测系统研究
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国家柑橘产业技术体系项目(CARS-Citrus)、国家重点研发计划项目(2021YFD1400802-4、2020YFD1000101、2021YFD1400802-44)和柑橘全程机械化科研基地建设项目(农计发[2017]19号)


Research on Asian Citrus Psyllid YOLO v8-MC Recognition Algorithm and Insect Remote Monitoring System
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    摘要:

    柑橘木虱是黄龙病的主要传播媒介,其发生与活动可对柑橘果园造成毁灭性后果。为实现木虱虫情的高效监测,设计了一种集诱捕拍照、耗材更新、害虫识别与结果展示于一体的智能监测系统。设计了具备诱虫胶带自动更新、虫情图像实时获取功能的诱捕监测装置;应用选点裁剪、Mosaic数据增强(Mosaic data augmentation,MDA)和CA(Coordinate attention)注意力机制,改进了YOLO v8木虱识别模型;开发了Web和手机APP客户端,可实现虫情数据的可视化展示与远程控制。模型测试阶段,改进后的YOLO v8-MC召回率、F1值及精确率分别达到91.20%、91%、90.60%,较基准模型分别提升5.47、5、4.64个百分点;迁移试验中,模型召回率、F1值及精确率分别达到88.64%、87%、84.78%,且系统工作状态良好,满足野外使用需求。开发的智能监测系统能有效实现果园木虱虫情的远程监测,可为此类虫害防治管理提供有效手段。

    Abstract:

    The Asian citrus psyllid (ACP) serves as the primary vector for Huanglongbing (HLB), a citrus tree disease with potentially devastating consequences for citrus orchards. In order to achieve efficient monitoring of ACP populations, an intelligent monitoring system capable of insect trapping, pest identification, and result visualization was developed. A monitoring device equipped with an automatic renewal mechanism for the insect trapping tape and real-time image capturing was designed. To improve the performance of the YOLO v8 model for ACP recognition, targeted cropping and Mosaic data augmentation techniques were employed to effectively expand the ACP dataset, addressing issues related to limited sample size and constrained positioning in the datasets. The application of a coordinate attention (CA) mechanism guided the model to comprehensively consider both channel and spatial information, thereby enhancing its ability to accurately locate the target psyllids. Additionally, the Web interface and mobile APP were developed to enable data visualization and remote control. During the model testing phase, the improved YOLO v8-MC achieved significant better performance than the baseline model, reaching 91.20%, 91%, and 90.60% in terms of recall rate, F1 score, and precision, respectively. In the field experiment, the model exhibited a recall rate of 88.64%, an F1 score of 87% and a precision of 84.78%, and the system operated effectively, meeting the requirements for field applications. In conclusion, the intelligent monitoring system developed enabled remote monitoring of ACP populations in orchards, providing an efficient mehtod for the management and control of such pest infestations.

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李善军,梁千月,余勇华,陈耀晖,付慧敏,张宏宇.柑橘木虱YOLO v8-MC识别算法与虫情远程监测系统研究[J].农业机械学报,2024,55(6):210-218. LI Shanjun, LIANG Qianyue, YU Yonghua, CHEN Yaohui, FU Huimin, ZHANG Hongyu. Research on Asian Citrus Psyllid YOLO v8-MC Recognition Algorithm and Insect Remote Monitoring System[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(6):210-218.

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  • 收稿日期:2023-11-02
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  • 在线发布日期: 2024-06-10
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