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Journal of Integrative Agriculture  2023, Vol. 22 Issue (7): 2173-2187    DOI: 10.1016/j.jia.2022.12.003
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Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision

LÜ Chun-yang1, 2, GE Shi-shuai2, 3, HE Wei2, 3, ZHANG Hao-wen1, 2, YANG Xian-ming2, CHU Bo2, 4, WU Kong-ming2#

1 State 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
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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.

Keywords:  Spodoptera frugiperda       computer vision        ovary        testis        WeChat applet  
Received: 29 July 2022   Accepted: 28 October 2022
Fund: This research was supported by the National Natural Science Foundation of China (31727901), the National Key R&D Program of China (2021YFD1400702) and the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.
About author:  LÜ Chun-yang, Mobile: +86-13718651161, E-mail:; #Correspondence WU Kong-ming, E-mail:

Cite this article: 

LÜ Chun-yang, GE Shi-shuai, HE Wei, ZHANG Hao-wen, YANG Xian-ming, CHU Bo, WU Kong-ming. 2023. Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision. Journal of Integrative Agriculture, 22(7): 2173-2187.

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