{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:20:14Z","timestamp":1776108014866,"version":"3.50.1"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Foundation","doi-asserted-by":"publisher","award":["NNF22OC0077040"],"award-info":[{"award-number":["NNF22OC0077040"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ecological Informatics"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.ecoinf.2026.103664","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:43:56Z","timestamp":1771519436000},"page":"103664","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Insect size matters: Using image and dimensions together improves image classification"],"prefix":"10.1016","volume":"94","author":[{"given":"Melika","family":"Baghooee","sequence":"first","affiliation":[]},{"given":"Quentin","family":"Geissmann","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ecoinf.2026.103664_bb0005","series-title":"Methods Ecol. Evol","first-page":"922","article-title":"Automatic image-based identification and biomass estimation of invertebrates","volume":"11","author":"\u00c4rje","year":"2020"},{"issue":"6","key":"10.1016\/j.ecoinf.2026.103664_bb0010","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.tig.2020.03.005","article-title":"Opening the black box: interpretable machine learning for geneticists","volume":"36","author":"Azodi","year":"2020","journal-title":"Trends Genet."},{"issue":"6","key":"10.1016\/j.ecoinf.2026.103664_bb0015","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1111\/2041-210X.14104","article-title":"Classifying the unknown: insect identification with deep hierarchical Bayesian learning","volume":"14","author":"Badirli","year":"2023","journal-title":"Methods Ecol. Evol."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0020","series-title":"Insects (Collins gem)","author":"Chinery","year":"2012"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0025","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: transformers for image recognition at scale (arXiv:2010.11929). arXiv Doi:10.48550\/arXiv.2010.11929."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0030","series-title":"2020 IEEE 23rd international conference on information FUSION (FUSION), 1\u20136","article-title":"Early vs late fusion in multimodal convolutional neural networks","author":"Gadzicki","year":"2020"},{"issue":"7","key":"10.1016\/j.ecoinf.2026.103664_bb0035","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pbio.3001689","article-title":"Sticky pi is a high-frequency smart trap that enables the study of insect circadian activity under natural conditions","volume":"20","author":"Geissmann","year":"2022","journal-title":"PLoS Biol."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0040","first-page":"43593","article-title":"A step towards worldwide biodiversity assessment: The BIOSCAN-1M insect dataset","volume":"36","author":"Gharaee","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0045","first-page":"36285","article-title":"Bioscan-5m: A multimodal dataset for insect biodiversity","volume":"37","author":"Gharaee","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0050","series-title":"International Conference on Machine Learning","first-page":"1321","article-title":"On calibration of modern neural networks","author":"Guo","year":"2017"},{"key":"10.1016\/j.ecoinf.2026.103664_bib176","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1002\/ece3.5921","article-title":"Species-level image classification with convolutional neural network enables insect identification from habitus images","volume":"10","author":"Hansen","year":"2020","journal-title":"Ecol. Evol."},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103664_bb0055","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting black-box models: a review on explainable artificial intelligence","volume":"16","author":"Hassija","year":"2024","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0060","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0065","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015b, December). Delving deep into rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile. doi:https:\/\/doi.org\/10.1109\/iccv.2015.123.","DOI":"10.1109\/ICCV.2015.123"},{"issue":"2","key":"10.1016\/j.ecoinf.2026.103664_bb0070","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2002545117","article-title":"Deep learning and computer vision will transform entomology","volume":"118","author":"H\u00f8ye","year":"2021","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0075","series-title":"Computer Vision \u2013 ACCV 2022","first-page":"425","article-title":"Rove-Tree-11: The not-so-wild rover a hierarchically structured image dataset for deep metric learning research","volume":"13845","author":"Hunt","year":"2023"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0080","series-title":"Bayesian Analysis for the Social Sciences","author":"Jackman","year":"2009"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0085","series-title":"Insect Identification in the Wild: The AMI Dataset","author":"Jain","year":"2025"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0090","author":"Jocher"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0095","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A convnet for the 2020s (arXiv:2201.03545). arXiv Doi:10.48550\/arXiv.2201.03545.","DOI":"10.1109\/CVPR52688.2022.01167"},{"issue":"9","key":"10.1016\/j.ecoinf.2026.103664_bb0100","doi-asserted-by":"crossref","first-page":"6530","DOI":"10.1109\/TGRS.2019.2906883","article-title":"Hydra: an Ensemble of Convolutional Neural Networks for geospatial land classification","volume":"57","author":"Minetto","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0105","article-title":"Attended temperature scaling: a practical approach for calibrating deep neural networks (arXiv:1810.11586)","author":"Mozafari","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0110","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., K\u00f6pf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., \u2026 Chintala, S. (2019). Pytorch: an imperative style, high-performance deep learning library (arXiv:1912.01703). arXiv Doi:10.48550\/arXiv.1912.01703."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0115","doi-asserted-by":"crossref","DOI":"10.3897\/BDJ.4.e9559","article-title":"iCollections \u2013 digitising the British and Irish butterflies in the Natural History Museum, London","volume":"4","author":"Paterson","year":"2016","journal-title":"Biodivers. Data J."},{"issue":"379\u2013423","key":"10.1016\/j.ecoinf.2026.103664_bb0125","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0130","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition (arXiv:1409.1556). arXiv Doi:10.48550\/arXiv.1409.1556."},{"issue":"18","key":"10.1016\/j.ecoinf.2026.103664_bb0135","doi-asserted-by":"crossref","first-page":"R738","DOI":"10.1016\/j.cub.2011.06.006","article-title":"Polyphenism in insects","volume":"21","author":"Simpson","year":"2011","journal-title":"Curr. Biol."},{"issue":"4","key":"10.1016\/j.ecoinf.2026.103664_bb0140","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0295474","article-title":"Insect detect: an open-source DIY camera trap for automated insect monitoring","volume":"19","author":"Sittinger","year":"2024","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.ecoinf.2026.103664_bb0145","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1146\/annurev-ento-112408-085500","article-title":"Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: from physiology to evolution","volume":"55","author":"Stillwell","year":"2010","journal-title":"Annu. Rev. Entomol."},{"key":"10.1016\/j.ecoinf.2026.103664_bb0150","series-title":"Proceedings of the 36th International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"Tan","year":"2019"},{"issue":"2","key":"10.1016\/j.ecoinf.2026.103664_bb0155","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1111\/2041-210X.13335","article-title":"Thinking like a naturalist: enhancing computer vision of citizen science images by harnessing contextual data","volume":"11","author":"Terry","year":"2020","journal-title":"Methods Ecol. Evol."},{"issue":"10","key":"10.1016\/j.ecoinf.2026.103664_bb0160","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.tree.2022.06.001","article-title":"Emerging technologies revolutionise insect ecology and monitoring","volume":"37","author":"Van Klink","year":"2022","journal-title":"Trends Ecol. Evol."},{"issue":"1904","key":"10.1016\/j.ecoinf.2026.103664_bb0165","doi-asserted-by":"crossref","first-page":"20230101","DOI":"10.1098\/rstb.2023.0101","article-title":"Towards a toolkit for global insect biodiversity monitoring","volume":"379","author":"Van Klink","year":"2024","journal-title":"Philos. Trans. R. Soc. B"},{"key":"10.1016\/j.ecoinf.2026.103664_bb0170","doi-asserted-by":"crossref","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."},{"issue":"3","key":"10.1016\/j.ecoinf.2026.103664_bb0175","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/1365-2656.12780","article-title":"A computer vision for animal ecology","volume":"87","author":"Weinstein","year":"2018","journal-title":"J. Anim. Ecol."}],"container-title":["Ecological Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954126000701?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1574954126000701?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:49:19Z","timestamp":1776102559000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1574954126000701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":35,"alternative-id":["S1574954126000701"],"URL":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103664","relation":{},"ISSN":["1574-9541"],"issn-type":[{"value":"1574-9541","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Insect size matters: Using image and dimensions together improves image classification","name":"articletitle","label":"Article Title"},{"value":"Ecological Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ecoinf.2026.103664","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103664"}}