{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T02:38:35Z","timestamp":1789699115058,"version":"4.0.1"},"reference-count":59,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Research Program of State Key Laboratory of Agricultural and Forestry Biosecurity","award":["SKLJRP2509"],"award-info":[{"award-number":["SKLJRP2509"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFC2606600"],"award-info":[{"award-number":["2023YFC2606600"]}]},{"name":"2115 Talent Development Program of China Agricultural University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Insects"],"abstract":"<jats:p>The morphological identification of lepidopteran pest pupae has long been a difficult task. To explore automated solutions, this study established a standardized, multi-angle image dataset of pupae from 11 economically important lepidopteran pests. We then systematically evaluated six deep learning models, including both convolutional neural networks and Transformer architectures. The results show that all models successfully learned to distinguish the vast majority of species, with Vit-Small achieving the highest accuracy (98.71 \u00b1 0.16%) and the highest F1-score (98.69 \u00b1 0.20%). This confirms that pupal morphology provides sufficient discriminative visual information to support highly accurate automated identification. However, all models exhibited consistent, minor confusion among Helicoverpa armigera, Mythimna separata and Spodoptera exigua. Analysis revealed these errors originated from specific viewing angles of a limited number of specimens, underscoring the value of the multi-angle imaging protocol used in this study. This study transforms pupal identification from a traditional taxonomic difficulty into a solvable computer vision task, providing a dataset, methodological benchmarks, and a feasibility validation for developing image-based tools for pupal-stage pest surveillance.<\/jats:p>","DOI":"10.3390\/insects17030327","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T12:42:16Z","timestamp":1773751336000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Image Classification of Pupae from 11 Lepidoptera Pest Species"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7576-1297","authenticated-orcid":false,"given":"Zitao","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Agricultural and Forestry Biosecurity, College of Plant Protection, China Agricultural University, Beijing 100193, China"},{"name":"Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0622-2064","authenticated-orcid":false,"given":"Xuankun","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Agricultural and Forestry Biosecurity, College of Plant Protection, China Agricultural University, Beijing 100193, China"},{"name":"Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1146\/annurev.ento.51.110104.151104","article-title":"Molecular mechanisms of metabolic resistance to synthetic and natural xenobiotics","volume":"52","author":"Li","year":"2007","journal-title":"Annu. Rev. Entomol."},{"key":"ref_2","first-page":"100286","article-title":"Lepidopterans of economic importance in Cameroon: A systematic review","volume":"8","author":"Ouaba","year":"2022","journal-title":"J. Agric. Food Res."},{"key":"ref_3","unstructured":"Doane, C.C., and McManus, M.L. (1981). The Gypsy Moth: Research Toward Integrated Pest Management, US Department of Agriculture. [1st ed.]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s10530-016-1325-9","article-title":"Global range expansion of pest Lepidoptera requires socially acceptable solutions","volume":"19","author":"Suckling","year":"2017","journal-title":"Biol. Invasions"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1093\/aesa\/saz031","article-title":"Pest injury guilds, Lepidoptera, and placing fruit-piercing moths in context: A review","volume":"112","author":"Klem","year":"2019","journal-title":"Ann. Entomol. Soc. Am."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tay, W.T., Soria, M.F., Walsh, T., Thomazoni, D., Silvie, P., Behere, G.T., Anderson, C., and Downes, S. (2013). A brave new world for an old world pest: Helicoverpa armigera (Lepidoptera: Noctuidae) in Brazil. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0080134"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.18311\/jbc\/2018\/21707","article-title":"Studies on new invasive pest Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae) and its natural enemies","volume":"32","author":"Shylesha","year":"2018","journal-title":"J. Biol. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1146\/annurev.ento.43.1.243","article-title":"Integrated pest management: Historical perspectives and contemporary developments","volume":"43","author":"Kogan","year":"1998","journal-title":"Annu. Rev. Entomol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1098\/rstb.2005.1713","article-title":"DNA barcodes for biosecurity: Invasive species identification","volume":"360","author":"Armstrong","year":"2005","journal-title":"Philos. Trans. R. Soc. B"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/00222937100770031","article-title":"A contribution towards an understanding of the morphology and phylogeny of the Ditrysian Lepidoptera","volume":"5","author":"Brock","year":"1971","journal-title":"J. Nat. Hist."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1086\/394630","article-title":"The morphology and development of the wing pattern of Lepidoptera","volume":"16","author":"Caspari","year":"1941","journal-title":"Q. Rev. Biol."},{"key":"ref_12","unstructured":"Miller, J.S. (1991). Cladistics and Classification of the Notodontidae (Lepidoptera: Noctuoidea) Based on Larval and Adult Morphology, American Museum of Natural History."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mosher, E. (1915). A Classification of the Lepidoptera Based on Characters of the Pupa, Illinois State Laboratory of Natural History.","DOI":"10.5962\/bhl.title.70830"},{"key":"ref_14","unstructured":"Pato\u010dka, J., and Tur\u010d\u00e1ni, M. (2023). Lepidoptera Pupae: Central European Species, Brill."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e2203230119","DOI":"10.1073\/pnas.2203230119","article-title":"Pest population dynamics are related to a continental overwintering gradient","volume":"119","author":"Lawton","year":"2022","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1111\/syen.12543","article-title":"Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)","volume":"47","author":"Popkov","year":"2022","journal-title":"Syst. Entomol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1038\/s41597-022-01627-5","article-title":"An annotated image dataset of medically and forensically important flies for deep learning model training","volume":"9","author":"Ong","year":"2022","journal-title":"Sci. Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1038\/s41597-022-01541-w","article-title":"An annotated image dataset for training mosquito species recognition system on human skin","volume":"9","author":"Ong","year":"2022","journal-title":"Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105585","DOI":"10.1016\/j.compag.2020.105585","article-title":"Pest24: A large-scale very small object data set of agricultural pests for multi-target detection","volume":"175","author":"Wang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1111\/mve.12682","article-title":"Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species","volume":"37","author":"Ling","year":"2023","journal-title":"Med. Vet. Entomol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"IS24011","DOI":"10.1071\/IS24011","article-title":"Image-based recognition of parasitoid wasps using advanced neural networks","volume":"38","author":"Shirali","year":"2024","journal-title":"Invertebr. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1111\/2041-210X.14235","article-title":"PENet: A phenotype encoding network for automatic extraction and representation of morphological discriminative features","volume":"14","author":"Zhao","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"172877","DOI":"10.1016\/j.scitotenv.2024.172877","article-title":"Automated identification of aquatic insects: A case study using deep learning and computer vision techniques","volume":"935","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kodors, S., Apeinans, I., Vancans, I., Bartulsons, T., and Zarembo, I. (2025, January 21\u201323). Early detection of caterpillars using artificial intelligence. Proceedings of the 24th International Scientific Conference\u201d Engineering for Rural Development\u201d, Jelgava, Latvia.","DOI":"10.22616\/ERDev.2025.24.TF112"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/agriculture12111919"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, H., Zhu, S., Shen, L., Chang, X., Wang, Y., Zeng, D., Xiong, B., Dai, F., and Zhao, T. (2023). Integrated analysis of machine learning and deep learning in silkworm pupae (Bombyx mori) species and sex identification. Animals, 13.","DOI":"10.3390\/ani13233612"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/00387010.2024.2447515","article-title":"Simultaneous identification of sex and species of silkworm pupae using hyperspectral imaging with PCA-t-SNE dimensionality reduction method","volume":"58","author":"Liu","year":"2025","journal-title":"Spectrosc. Lett."},{"key":"ref_28","unstructured":"Oliver, A. (1981). Biology and Illustrated Key for the Identification of Twenty Species of Economically Important Noctuid Pests, Louisiana State University Agricultural Experiment Station. [1st ed.]."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"748","DOI":"10.5958\/0974-8172.2018.00188.8","article-title":"Taxonomy of agriculturally important plusiinae (Lepidoptera: Noctuidae)","volume":"80","author":"Twinkle","year":"2018","journal-title":"Indian J. Entomol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, H., Chen, Z., Zhao, Y., Liu, Y., Xiong, H., Duan, Y., Song, F., Cai, W., and Li, X. (2026). Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: A case study on assassin bugs (Hemiptera: Reduviidae). Cladistics, early view.","DOI":"10.1111\/cla.70029"},{"key":"ref_31","unstructured":"Jocher, G., Qiu, J., and Chaurasia, A. (2026, January 01). YOLO11 by Ultralytics. 2024, Version 11.0.0. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_32","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_33","unstructured":"Wang, W. (2026, January 01). X-AnyLabeling, 2025, Version 3.3.1. Available online: https:\/\/github.com\/CVHub520\/X-AnyLabeling."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_35","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_36","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_40","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gundersen, O.E., Shamsaliei, S., Kj\u00e6rnli, H.S., and Langseth, H. (2023, January 27\u201329). On reporting robust and trustworthy conclusions from model comparison studies involving neural networks and randomness. Proceedings of the 2023 ACM Conference on Reproducibility and Replicability, Santa Cruz, CA, USA.","DOI":"10.1145\/3589806.3600044"},{"key":"ref_43","unstructured":"Picard, D. (2021). Torch.manual_seed (3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision. arXiv."},{"key":"ref_44","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."},{"key":"ref_45","unstructured":"Loshchilov, I., and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. B Stat. Methodol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107440","DOI":"10.1016\/j.compag.2022.107440","article-title":"A deep learning and Grad-Cam-based approach for accurate identification of the fall armyworm (Spodoptera frugiperda) in maize fields","volume":"202","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","first-page":"396","article-title":"Advanced classification of agricultural plant insects using deep learning and explainability","volume":"13","author":"Vo","year":"2025","journal-title":"Indones. J. Electr. Eng. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1016\/S2095-3119(19)62862-5","article-title":"Morphological traits for sex identification of the oriental armyworm, Mythimna separata (Lepidoptera: Noctuidae)","volume":"19","author":"Chen","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_54","first-page":"209","article-title":"Pupation behaviour, depth, and site of Spodoptera exigua","volume":"64","author":"Zheng","year":"2011","journal-title":"Bull. Insectology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s13744-017-0581-4","article-title":"Morphological Characterization of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae: Heliothinae)","volume":"47","author":"Casagrande","year":"2018","journal-title":"Neotrop. Entomol."},{"key":"ref_56","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., and Brendel, W. (May, January 30). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Proceedings of the International conference on learning representations, Vancouver, BC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4683","DOI":"10.1109\/TIP.2020.2973812","article-title":"The devil is in the channels: Mutual-channel loss for fine-grained image classification","volume":"29","author":"Chang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_59","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."}],"container-title":["Insects"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4450\/17\/3\/327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T05:23:47Z","timestamp":1773897827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4450\/17\/3\/327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":59,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["insects17030327"],"URL":"https:\/\/doi.org\/10.3390\/insects17030327","relation":{},"ISSN":["2075-4450"],"issn-type":[{"value":"2075-4450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]}}}