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Integrating weight and imaging features: A machine learning framework for larval instar identification in Mythimna separata (Walker)

Published online by Cambridge University Press:  23 April 2025

Xiao Feng ,
Jingyu Wang ,
Yunliang Ji ,
Sohail Abbas ,
Cong Zhang ,
Jamin Ali ,
Adil Tonga ,
Rizhao Chen Open the ORCID record for Rizhao Chen [Opens in a new window]  and
Qiyun Li
Xiao Feng
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Jingyu Wang
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Yunliang Ji
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Sohail Abbas
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Cong Zhang
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Jamin Ali
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Adil Tonga
Affiliation:
Entomology Department, Diyarbakir Plant Protection Research Institute, Diyarbakir, Türkiye
Rizhao Chen*
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
Qiyun Li*
Affiliation:
College of Plant Protection, Jilin Agricultural University, Changchun, PR China
*
Corresponding author: Rizhao Chen; Email: [email protected]
Qiyun Li; Email: [email protected]
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Abstract

The oriental armyworm, Mythimna separata (Walker), is a highly migratory pest known for its sudden larval outbreaks, which result in severe crop losses. These unpredictable surges pose significant challenges for timely and accurate monitoring, as conventional methods are labour-intensive and prone to errors. To address these limitations, this study investigates the use of machine learning for automated and precise identification of M. separata larval instars. A total of 1577 larval images representing different instar were analysed for geometric, colour, and texture features. Additionally, larval weight was predicted using 13 regression models. Instar identification was conducted using Support Vector Classifier (SVC), Random Forest, and Multi-Layer Perceptron. Key feature contributing to classification accuracy were subsequently identified through permutation feature importance analysis. The results demonstrated the potential of machine learning for automating instar identification with high efficiency and accuracy. Predicted larval weight emerged as a key feature, significantly enhancing the performance of all identification models. Among the tested approaches, BaggingRegressor exhibited the best performance for larval weight prediction (R2 = 98.20%, RMSE = 0.2313), while SVC achieved the highest instar identification accuracy (94%). Overall, the integration of larval weight with other image-derived features proved to be a highly effective strategy. This study demonstrates the efficacy of machine learning in enhancing pest monitoring systems by providing a scalable and reliable framework for precise pest management. The proposed methodology significantly improves larval instar identification accuracy and efficiency, offering actionable insights for implementing targeted biological and chemical control strategies.

Keywords

instar identificationlarval weightmachine learningpermutation feature importanceShapley additive explanations
Type
Research Paper
Information
Bulletin of Entomological Research , First View , pp. 1 - 10
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Copyright
© The Author(s), 2025. Published by Cambridge University Press.

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Footnotes

†

These authors contributed equally to this paper.

References

Behmann, J, Mahlein, A-K, Rumpf, T, Römer, C and Plümer, L (2015) A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 16(3), 239–260. doi:10.1007/s11119-014-9372-7CrossRefGoogle Scholar
Benkwitz-Bedford, S, Palm, M, Demirtas, TY, Mustonen, V, Farewell, A, Warringer, J, Parts, L and Moradigaravand, D (2021) Machine learning prediction of resistance to subinhibitory antimicrobial concentrations from Escherichia coli genomes. Msystems 6(4). doi:10.1128/msystems.00346-21CrossRefGoogle ScholarPubMed
Chen, Y, Chen, M, Guo, M, Wang, F and Wang, J (2023) A multi-stage prediction framework for pest identification. In: 2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS).CrossRefGoogle Scholar
Choe, HO, Lee, MH and Yoe, H (2023) Automatic pest image acquisition system hardware design. In: 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).CrossRefGoogle Scholar
Dong, S, Du, J, Jiao, L, Wang, F, Liu, K, Teng, Y and Wang, R (2022) Automatic crop pest detection oriented multiscale feature fusion approach. Insects 13(6), 554.CrossRefGoogle ScholarPubMed
Duan, H, Dai, X, Shi, Q, Cheng, Y, Ge, Y, Chang, S, Liu, W, Wang, F, Shi, H and Hu, J (2024) Enhancing genome-wide populus trait prediction through deep convolutional neural networks. The Plant Journal 119(2), 735–745. doi:10.1111/tpj.16790CrossRefGoogle ScholarPubMed
Gao, Y, Xue, X, Qin, G, Li, K, Liu, J, Zhang, Y and Li, X (2024) Application of machine learning in automatic image identification of insects - a review. Ecological Informatics 80, 102539. doi:10.1016/j.ecoinf.2024.102539CrossRefGoogle Scholar
Gomes, JC and Borges, DL (2022) Insect pest image recognition: A few-shot machine learning approach including maturity stages classification. Agronomy 12(8), 1733.CrossRefGoogle Scholar
Huang, Y, Lan, Y, Thomson, SJ, Fang, A, Hoffmann, WC and Lacey, RE (2010) Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture 71(2), 107–127. doi:10.1016/j.compag.2010.01.001CrossRefGoogle Scholar
Johari, SNAM, Khairunniza-Bejo, S, Shariff, ARM, Husin, NA, Masri, MMM and Kamarudin, N (2023) Automatic classification of bagworm, Metisa plana (Walker) instar stages using a transfer learning-based framework. Agriculture 13(2), 442.CrossRefGoogle Scholar
John, M, Bankole, I, Ajayi-Moses, O, Ijila, T, Jeje, T and Patil, L (2023) Relevance of advanced plant disease detection techniques in disease and pest management for ensuring food security and their implication: A review. American Journal of Plant Sciences, 14. doi:10.4236/ajps.2023.1411086Google Scholar
Kabir, MH, Guindo, ML, Chen, R and Liu, F (2021) Geographic origin discrimination of millet using Vis-NIR spectroscopy combined with machine learning techniques. Foods 10(11), 2767.CrossRefGoogle ScholarPubMed
Kariyanna, B and Sowjanya, M (2024) Unravelling the use of artificial intelligence in management of insect pests. Smart Agricultural Technology 8, 100517. doi:10.1016/j.atech.2024.100517CrossRefGoogle Scholar
Li, J, Coudron, TA, Pan, W, Liu, X, Lu, Z and Zhang, Q (2006) Host age preference of Microplitis mediator (Hymenoptera: Braconidae), an endoparasitoid of Mythimna separata (Lepidoptera: Noctuidae). Biological Control 39(3), 257–261. doi:10.1016/j.biocontrol.2006.09.002CrossRefGoogle Scholar
Li, W, Zheng, T, Yang, Z, Li, M, Sun, C and Yang, X (2021) Classification and detection of insects from field images using deep learning for smart pest management: A systematic review. Ecological Informatics 66, 101460. doi:10.1016/j.ecoinf.2021.101460CrossRefGoogle Scholar
Lin, C (1990) Ecophysiology of Mythimna Separata. Beijing: Beijing University Press.Google Scholar
Liu, J, Shi, Y, Tian, Z, Li, F, Hao, Z, Wen, W, Zhang, L, Wang, Y, Li, Y and Fan, Z (2022) Bioactivity-guided synthesis accelerates the discovery of evodiamine derivatives as potent insecticide candidates. Journal of Agricultural and Food Chemistry 70(16), 5197–5206. doi:10.1021/acs.jafc.1c08297CrossRefGoogle ScholarPubMed
LU, S, and YE, S. (2020). Using an image segmentation and support vector machine method for identifying two locust species and instars. Journal of Integrative Agriculture, 19(5), 1301–1313. doi:10.1016/S2095-3119(19)62865-0CrossRefGoogle Scholar
Majewski, P, Zapotoczny, P, Lampa, P, Burduk, R and Reiner, J (2022) Multipurpose monitoring system for edible insect breeding based on machine learning. Scientific Reports 12(1), 7892. doi:10.1038/s41598-022-11794-5CrossRefGoogle ScholarPubMed
Mishra, H, and Mishra, D (2024) AI for data-driven decision-making in smart agriculture: From field to farm management. In Chouhan, Siddharth Singh, Saxena, Akash, Singh, Uday Pratap, Jain, Sanjeev (eds.), Artificial Intelligence Techniques in Smart Agriculture. Singapore: Springer Nature Singapore, 173–193.CrossRefGoogle Scholar
Moradigaravand, D, Li, L, Dechesne, A, Nesme, J, de la Cruz, R, Ahmad, H, Banzhaf, M, Sørensen, S, Smets, B and Kreft, J-U (2023) Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning. Bioinformatics, 39. doi:10.1093/bioinformatics/btad400Google ScholarPubMed
Nurul Afiah Mohd Johari, S, Khairunniza-Bejo, S, Rashid Mohamed Shariff, A, Azuan Husin, N, Mazmira Mohd Basri, M and Kamarudin, N (2022) Identification of bagworm (Metisa plana) instar stages using hyperspectral imaging and machine learning techniques. Computers and Electronics in Agriculture 194, 106739. doi:10.1016/j.compag.2022.106739CrossRefGoogle Scholar
Ong, S-Q, Ahmad, H and Majid, AHA (2021) Development of a deep learning model from breeding substrate images: A novel method for estimating the abundance of house fly (L.) larvae. Pest Management Science 77(12), 5347–5355. doi:10.1002/ps.6573CrossRefGoogle ScholarPubMed
Patil, J, Vijayakumar, R, Linga, V and Sivakumar, G (2020) Susceptibility of Oriental armyworm, Mythimna separata (Lepidoptera: Noctuidae) larvae and pupae to native entomopathogenic nematodes. Journal of Applied Entomology 144(7), 647–654. doi:10.1111/jen.12786CrossRefGoogle Scholar
Qin, W-b, Abbas, A, Abbas, S, Alam, A, Chen, D-H, Hafeez, F, Ali, J, Romano, D and Chen, R-Z (2024) Automated lepidopteran pest developmental stages classification via transfer learning framework. Environmental Entomology 53(6), 1062–1077. doi:10.1093/ee/nvae085CrossRefGoogle ScholarPubMed
Salimi, A, Rostami, J, Moormann, C and Delisio, A (2016) Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs. Tunnelling & Underground Space Technology 58, 236–246. doi:10.1016/j.tust.2016.05.009CrossRefGoogle Scholar
Saqib, M (2021) Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model. Applied Intelligence 51(5), 2703–2713. doi:10.1007/s10489-020-01942-7CrossRefGoogle ScholarPubMed
Tian, H, Wang, T, Liu, Y, Qiao, X and Li, Y (2020) Computer vision technology in agricultural automation—A review. Information Processing in Agriculture 7(1), 1–19. doi:10.1016/j.inpa.2019.09.006CrossRefGoogle Scholar
Xu, J, Feng, Z, Tang, J, Liu, S, Ding, Z, Lyu, J, Yao, Q and Yang, B (2022) Improved random forest for the automatic identification of Spodoptera frugiperda larval instar stages. Agriculture 12, 1919. doi:10.3390/agriculture12111919CrossRefGoogle Scholar
Ye, S, Lu, S, Bai, X and Gu, J (2020) ResNet-locust-BN network-based automatic identification of East Asian migratory locust species and instars from RGB images. Insects 11(8), 458.CrossRefGoogle Scholar
Zhou, J, Huang, H, Sun, Y, Chu, J, Zhang, W, Qu, F and Yang, H (2024a) Mutual learning with memory for semi-supervised pest detection. Frontiers in Plant Science 15. doi:10.3389/fpls.2024.1369696CrossRefGoogle Scholar
Zhou, W, Arcot, Y, Medina, RF, Bernal, J, Cisneros-Zevallos, L and Akbulut, MES (2024b) Integrated pest management: an update on the sustainability approach to crop protection. ACS Omega 9(40), 41130–41147. doi:10.1021/acsomega.4c06628CrossRefGoogle Scholar
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