Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky board images and an improved Cascade R-CNN

作     者：Yufan Gao Fei Yin Chen Hong Xiangfu Chen Hang Deng Yongjian Liu Zhenyu Li Qing Yao Yufan Gao;Fei Yin;Chen Hong;Xiangfu Chen;Hang Deng;Yongjian Liu;Zhenyu Li;Qing Yao

作者机构：School of Computer Science and TechnologyZhejiang Sci-Tech UniversityHangzhou 310018China Collaborative Innovation CenterGuangdong Academy of Agricultural SciencesGuangzhou 510640China 

出 版 物：《Journal of Integrative Agriculture》 (农业科学学报(英文版))

年 卷 期：2025年第24卷第1期

页      面：220-234页

核心收录：

学科分类：12[管理学] 08[工学] 09[农学] 090401[农学-植物病理学] 090402[农学-农业昆虫与害虫防治] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 0904[农学-植物保护] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences China(XTXM202202) 

主　　题：vegetable pests yellow sticky boards intelligent monitoring system deep learning pest detection 

摘      要：Cruciferous vegetables are important edible vegetable ***,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific *** yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow *** achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a *** the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every *** pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model *** solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop *** algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck *** results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of *** with other algorithms,our algorithm has a significant advantage in its ability to detect small target *** accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was *** algorithm performed well and achieved