Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda(Lepidoptera:Noctuidae)based on computer vision  

作　　者：LÜChun-yang GE Shi-shuai HE Wei ZHANG Hao-wen YANG Xian-ming CHU Bo WU Kong-ming 

机构地区：[1]Key Laboratory of Agricultural Information Service Technology,Ministry of Agriculture and Rural Affairs,Agricultural Information Institute,Chinese Academy of Agricultural Sciences,Beijing 100081,P.R.China [2]State Key Laboratory for Biology of Plant Diseases and Insect Pests,Institute of Plant Protection,Chinese Academy of Agricultural Sciences,Beijing 100193,P.R.China [3]State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops,Institute of Applied Ecology,Fujian Agriculture and Forestry University,Fuzhou 350002,P.R.China [4]College of Plant Protection,Henan Agricultural University,Zhengzhou 450002,P.R.China

出　　处：《Journal of Integrative Agriculture》2023年第7期2173-2187,共15页农业科学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(31727901);the National Key R&D Program of China(2021YFD1400702);the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.

摘　　要：Spodoptera frugiperda(Lepidoptera:Noctuidae)is an important migratory agricultural pest worldwide,which has invaded many countries in the Old World since 2016 and now poses a serious threat to world food security.The present monitoring and early warning strategies for the fall army worm(FAW)mainly focus on adult population density,but lack an information technology platform for precisely forecasting the reproductive dynamics of the adults.In this study,to identify the developmental status of the adults,we first utilized female ovarian images to extract and screen five features combined with the support vector machine(SVM)classifier and employed male testes images to obtain the testis circular features.Then,we established models for the relationship between oviposition dynamics and the developmental time of adult reproductive organs using laboratory tests.The results show that the accuracy of female ovary development stage determination reached 91%.The mean standard error(MSE)between the actual and predicted values of the ovarian developmental time was 0.2431,and the mean error rate between the actual and predicted values of the daily oviposition quantity was 12.38%.The error rate for the recognition of testis diameter was 3.25%,and the predicted and actual values of the testis developmental time in males had an MSE of 0.7734.A WeChat applet for identifying the reproductive developmental state and predicting reproduction of S.frugiperda was developed by integrating the above research results,and it is now available for use by anyone involved in plant protection.This study developed an automated method for accurately forecasting the reproductive dynamics of S.frugiperda populations,which can be helpful for the construction of a population monitoring and early warning system for use by both professional experts and local people at the county level.

关 键 词：Spodoptera frugiperda computer vision OVARY TESTIS WeChat applet 

分 类 号：S433.4[农业科学—农业昆虫与害虫防治] TP391.41[农业科学—植物保护]