Please wait a minute...
Journal of Integrative Agriculture
Advanced Online Publication | Current Issue | Archive | Adv Search
Accurate recognition of the reproductive development status and prediction of oviposition fecundity in Spodoptera frugiperda (Lepidoptera: Noctuidae) based on computer vision

LV 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

Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      



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  
Online: 22 December 2022  

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:  LV Chun-yang, Tel: +86-13718651161, E-mail:; Correspondence WU Kong-ming, E-mail:

Cite this article: 

LV Chun-yang, GE Shi-shuai, HE Wei, ZHANG Hao-wen, YANG Xian-ming, CHU Bo, WU Kong-ming. 2022. 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, Doi:10.1016/j.jia.2022.12.003

Bochkovskiy A, Wang C Y, Liao H Y M. 2020. YOLOv4: Optimal speed and accuracy of object detection. Computer Science, arXiv preprint arXiv: 2004.10934.

Chen Q H, Zeng J, Zeng W, Li Q, Chen X J, Zhou Y. 2017. Application of the morphological indicators of the male internal reproductive system in forecasting the population dynamics of the rice leaf roller, Cnaphalocrocis medinalis (Lepidoptera: Pyralidae) by sex pheromone trapping. Acta Entomologica Sinica, 60, 927-935. (in Chinese)

Day R, Abrahams P, Bateman M, Beale T, Clottey V, Cock M, Witt A, Colmenarez Y, Corniani N, Early R, Godwin J, Gomez J, Moreno P G, Murphy S T, Oppong-Mensah B, Phiri N, Pratt C, Silvestri S, Witt A. 2017. Fall armyworm: Impacts and implications for Africa. Outlooks on Pest Management, 28, 196-201.

Dong Q J, Zhou J C, Zhu K H, Zhang Z T, Dong H. 2019. A simple method for identifying sexuality of Spodoptera frugiperda (J.E.Smith) pupae and adults. Plant Protection, 45, 96-98, 105. (in Chinese)

DPPMAM (Department of Plant Protection, Ministry of Agriculture, Myanmar). 2019. First detection report of the fall armyworm Spodoptera frugiperda (Lepidoptra: Noctuidae) on maize in Myanmar. [2019-01-14].

Duda R O, Hart P E. 1972. Use of the hough transformation to detect lines and curves in pictures. Cacm, 15, 11-15.

Early R, González-Moreno P, Murphy S T, Day R. 2018. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota, 40, 25-50.

FAO (Food and Agriculture Organization). 2018. First detection of fall army worm on the border of Thailand. [2018-12-19].

Feng L, Tang S S, Liu F, Dai C G, Xing J C, Li H B. 2022. Toxicity and control efficacy of seven bio-insecticides against Spodoptera frugiperda and Mythimna separata larvae. Journal of Environmental Entomology, 44, 35-43. (in Chinese)

Gao Y. 2017. Maize pest identification based on SVM and DS image data fusion. MSc thesis, Anhui Agricultural University, Hefei. (in Chinese)

Ge S S, He W, He, L M, Yan R, Zhang H W, Wu K M. 2021. Flight activity promotes reproductive processes in the fall armyworm, Spodoptera frugiperda. Journal of Integrative Agriculture, 20, 727-735.

Goergen G, Kumar P L, Sankung S B, Togola A, Tamò M. 2016. First report of outbreaks of the fall armyworm Spodoptera frugiperda (JE Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS ONE, 11, e0165632.

Hansen O L P, Svenning J C, Olsen K, Dupont S, Garner B H, Iosifidis A, Price B W, Høye T T. 2020. Species‐level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10, 737-747.

Haralick R M, Shanmugam K, Dinstein I H. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610-621.

He L M, Ge S S, Zhang H W, He W, Yan R, Wu K M. 2021. Photoregime affects development, reproduction, and flight performance of the invasive fall armyworm (Lepidoptera: Noctuidae) in China. Environmental Entomology, 50, 367-381.

He W, Zhao S Y, Ge S S, Jiang Y Y, Zhao X C, Wu K M. 2019. Population prediction method using sexual trapping for Spodoptera frugiperda. Plant Protection, 45, 48-53, 115. (in Chinese)

He W, Zhao X C, Ali A, Ge S S, Zhang H W, He L M, Wu K M. 2021a. Population dynamics and reproductive developmental analysis of Helicoverpa armigera (Lepidoptera: Noctuidae) trapped using food attractants in the field. Journal of Economic Entomology, 114, 1533-1541.

He W, Zhao X C, Ge S S, Wu K M. 2021b. Food attractants for field population monitoring of Spodoptera exigua (Hübner). Crop Protection, 145, 105616.

Hu X, Liu Y, Zhao Z, Liu J, Yang X, Sun C, Chen S H, Li B, Zhou C. 2021. Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Computers and Electronics in Agriculture, 185, 106135.

Jiang X F, Zhang L, Cheng Y X, Song L L. 2019. Advances in migration and monitoring techniques of the fall armyworm, Spodoptera frugiperda (J. E. Smith). Plant Protection, 45, 12-18. (in Chinese)

Jiang Y Y, Liu J, Xie M C, Li Y H, Yang J J, Zhang M L, Qiu K. 2019. Observation on law of diffusion damage of Spodoptera frugiperda in China in 2019. Plant Protection, 45, 10-19. (in Chinese) 

Jiang Y Y, Liu J, Yang J J, Zhao W X, Yi L, Liu Y, Ye S F, Qin B Q, Song L D. 2020. Trapping effect of searchlight-trap and light trap for the moth of Spodoptera frugiperda in 2019. Plant Protection, 46, 118-122, 156. (in Chinese)

Liang G M, Tan W J, Guo Y Y. 1999. Improvement of artificial raising of cotton bollworm. Crop Protection, 25, 3. (in Chinese)

Lin X Z, Zhang J Y, Zhu S H, Liu D Y. 2019. Sparse representation classification method of rice planthopper image based on K-SVD and orthogonal matching pursuit algorithm. Transactions of the Chinese Society of Agricultural Engineering, 35, 216-222. (in Chinese)

Liu J, Jiang Y Y, Liu W C, Li Y H, Zeng J, Yang Q B. 2019. Investigation and forecast techniques of Spodoptera frugiperda China. Plant Protection, 39, 44-47. (in Chinese)

Maktabdar Oghaz M, Maarof M A, Rohani M F, Zainal A, Shaid S Z M. 2019. An optimized skin texture model using gray-level co-occurrence matrix. Neural Computing and Applications, 31, 1835-1853.

Mohammadi S, Mohammadi M, Dehlaghi V, Ahmadi A. 2019. Automatic segmentation, detection, and diagnosis of abdominal aortic aneurysm (AAA) using convolutional neural networks and hough circles algorithm. Cardiovascular Engineering and Technology, 10, 490-499.

Montezano D G, Specht A, Sosa-Gómez D R, Roque-Specht V F, Sousa-Silva J C, Paula-Moraes S V, Peterson J A, Hunt T E. 2018. Host plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. African Entomology, 26, 286-300. 

Nagoshi R N, Fleischer S, Meagher R L. 2009. Texas is the overwintering source of fall armyworm in central Pennsylvania: implications for migration into the northeastern United States. Environmental Entomology, 38, 1546-1554.

Nagoshi R N, Murúa M G, Hay-Roe M, Juárez M L, Willink E, Meagher R L. 2012. Genetic characterization of fall armyworm (Lepidoptera: Noctuidae) host strains in Argentina. Journal of Economic Entomology, 105, 418-428.

Nakweya G. 2018. Global actions needed to combat fall armyworm. [2018-09-28].

Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66. 

Redmon J, Divvala S, Girshick R, Farhadi A. 2016. You only look once: Unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, USA. pp, 779-788.

Schaefer S, Yuksel C. 2007. Example-based skeleton extraction. In: Eurographics Symposium on Geometry Processing (2007), Eurographics Association and Wiley, Austria. pp, 153-162.

Sethy A, Patra P K, Nayak D R. 2019. Gray-level co-occurrence matrix and random forest based off-line Odia handwritten character recognition. Recent Patents on Engineering, 13, 136-141.

Shylesha A N, Jalali S K, Gupta A, Varshney R, Venkatesan T, Shetty P, Ojha R, C.Ganiger P, Navik O, Subaharan K, Bakthavatsalam N, Ballal C R.. 2018. Studies on new invasive pest Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae) and its natural enemies. Journal of Biological Control, 32, 145-151.

Sisay B, Simiyu J, Malusi P, Likhayo P, Mendesil E, Elibariki N, Wakgari M, Ayalew G, Tefera T. 2018. First report of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), natural enemies from Africa. Journal of Applied Entomology, 142, 800-804.

Sparks A N, Jackson R D, Carpenter J E, Muller R A. 1986. Insects captured in light traps in the Gulf of Mexico. Annals of the Entomological Society of America. 79, 132-139.

Stricker M A, Orengo M. 1995. Similarity of color images. In: Proceeding of Storage and Retrieval for Image and Video Databases III, The International Society for Optical Engine, California, America. pp. 381-392.

Sun X X, Hu C X, Jia H R, Wu Q L, Shen X J, Zhao S Y, Jiang Y Y, Wu K M. 2021. Case study on the first immigration of fall armyworm, Spodoptera frugiperda invading into China. Journal of Integrative Agriculture, 20, 664-672.

Tamez-Guerra P, Tamayo-Mejía F, Gomez-Flores R, Rodríguez-Padilla C, Damas G, Tamez-Guerra R. S, Ek-Ramos M, Williams T. 2017. Increased efficacy and extended shelf life of spinosad formulated in phagostimulant granules against Spodoptera frugiperda. Pest Management Science, 74, 100-110.

Wang H X, Li Y F, Dang L M, Moon H. 2022. An efficient attention module for instance segmentation network in pest monitoring. Computers and Electronics in Agriculture, 195, 106853.

Wu K M. 2020. Management strategies of fall armyworm (Spodoptera frugiperda) in China. Plant Protection, 46, 1-5. (in Chinese)

Wu K S, Otoo E, Suzuki K. 2009. Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 12, 117-135.

Yang B H, Gao Z W, Gao Y, Zhu Y. 2021. Rapid detection and counting of wheat ears in the field using YOLOv4 with attention module. Agronomy, 11, 1202.

Yang X M, Zhao S Y, Jiang Y Y, Wu K M. 2020. Population occurrence and sampling technique of fall armyworm Spodoptera frugiperda in barley field. Plant Protection, 46, 18-23. (in Chinese)

Zhang W N, Xiao H J, Liang G M, Guo Y Y. 2013. Observation on ovarian morphology and oogenesis in the cotton bollworm, Helicoverpa armigera (Lepidoptera: Noctuidae). Acta Entomologica Sinica, 56, 358-364.

Zhang X G. 2000. Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica, 26, 36-46. (in Chinese)

Zhao S Y, Yang X M, He W, Zhang H W, Jiang Y Y, Wu K M. 2019a. Ovarian development gradation and reproduction potential prediction in Spodoptera frugiperda. Plant Protection, 45, 28-34. (in Chinese)

Zhao S Y, Yang X M, Yang X L, Song Y F, Wu K M. 2019b. Field efficacy of eight insecticides on fall armyworm, Spodoptera frugiperda. Plant Protection, 45,74-78. (in Chinese)

Zhu L Q, Ma M Y, Zhang Z, Zhang P Y, Wu W, Wang D D, Zhang D X, Wang X, Wang H Y. 2017. Hybrid deep learning for automated lepidopteran insect image classification. Oriental Insects, 51, 79-91.

No related articles found!
No Suggested Reading articles found!