{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T13:35:44Z","timestamp":1785418544914,"version":"3.56.0"},"reference-count":21,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Yandex.Cloud company"},{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["24-66-00001"],"award-info":[{"award-number":["24-66-00001"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]},{"award":["24-66-00001"],"award-info":[{"award-number":["24-66-00001"]}],"id":[{"id":"https:\/\/ror.org\/03y2gwe85","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003443","name":"the Ministry of Education of the Russian Federation","doi-asserted-by":"crossref","award":["#FZZE-2026-0004"],"award-info":[{"award-number":["#FZZE-2026-0004"]}],"id":[{"id":"10.13039\/501100003443","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Foundation for Support of Applied Ecological Studies \u00abLake Baikal\u00bb"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Studying zooplankton in freshwater ecosystems is crucial for ecological research, providing insight into ecosystem health, biodiversity, and water quality. This study focuses on developing a hybrid approach for studying and analyzing zooplankton communities using machine learning and human expert analysis. The goal of the study was to automate the labor-intensive process of zooplankton analysis as part of a long-term Lake Baikal monitoring program (since 1945), while maintaining continuity with traditional methods. A software and algorithmic system were developed to automate the analysis: images were processed using a two-stage pipeline (object detection using YOLO V11, classification using metric learning and visual transformers), and complex cases and new species were sent to specialists for verification. Over 240,000 images from 811 samples were processed, and models are updated using verified data to adapt to seasonal changes. An updatable database of labeled zooplankton images suitable for statistical analysis and research has been created. A comparison of manual and machine analysis revealed no significant differences in species composition, with accurate detection in 87% of images. This approach allows for scalable monitoring and the accumulation of labeled data arrays for the development of computer vision methods and the assessment of the state of Lake Baikal\u2019s ecosystem.<\/jats:p>","DOI":"10.3390\/biology15090695","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:34:42Z","timestamp":1777458882000},"page":"695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computer Vision-Assisted Semiautomatic Analysis of Zooplankton in a Longitudinal Study of the Ecological State of Lake Baikal"],"prefix":"10.3390","volume":"15","author":[{"given":"Olga Olegovna","family":"Rusanovskaya","sequence":"first","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey Sergeevich","family":"Oreshkov","sequence":"additional","affiliation":[{"name":"MaritimeAI, Kaliningrad 236000, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7448-0199","authenticated-orcid":false,"given":"Anastasiya Andreevna","family":"Demidova","sequence":"additional","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taysia Pavlovna","family":"Rzhepka","sequence":"additional","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugene Anatolyevich","family":"Silow","sequence":"additional","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nickolai Vasilyevich","family":"Shadrin","sequence":"additional","affiliation":[{"name":"A.O. Kovalevsky Institute of Biology of Southern Seas of RAS, Sevastopol 299011, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana Vladimirovna","family":"Shimaraeva","sequence":"additional","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5250-6818","authenticated-orcid":false,"given":"Maxim Anatolyevich","family":"Timofeyev","sequence":"additional","affiliation":[{"name":"Institute of Lake Baikal Biology, Irkutsk State University, Irkutsk 664003, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kozhov, M.M. (1963). Lake Baikal and Its Life, Dr. W. 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Inland Waters"},{"key":"ref_6","first-page":"76","article-title":"Seasonal dynamics of zooplankton in the pelagic zone of Southern Baikal (opposite the village of Bolshye Koty)","volume":"76","author":"Demidova","year":"2025","journal-title":"Proc. Altai Branch Russ. Geogr. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Oldenburg, E., Kronberg, R.M., Niehoff, B., Ebenh\u04e7h, O., and Popa, O. (2023). DeepLOKI- a deep learning based approach to identify zooplankton taxa on high-resolution images from the optical plankton recorder LOKI. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1280510"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kyathanahally, S.P., Hardeman, T., Merz, E., Bulas, T., Reyes, M., Isles, P., Pomati, F., and Baity-Jesi, M. (2021). Deep learning classification of lake zooplankton. Front. Microbiol., 12.","DOI":"10.3389\/fmicb.2021.746297"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12142","DOI":"10.1038\/s41598-020-68662-3","article-title":"Annotation-free learning of plankton for classification and anomaly detection","volume":"10","author":"Pastore","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1093\/plankt\/fbp124","article-title":"Digital zooplankton image analysis using the ZooScan integrated system","volume":"32","author":"Gorsky","year":"2010","journal-title":"J. Plankton Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vogelmann, C., Barco, A., Knust, J.-M., and Stibor, H. (2024). Unraveling Zooplankton Diversity in a Pre-Alpine Lake: A Comparative Analysis of ZooScan and DNA Metabarcoding Methods. Water, 16.","DOI":"10.3390\/w16030411"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/lom3.10674","article-title":"Picturing plankton: Complementing net-based plankton community assessments with optical imaging across diverse marine environments","volume":"23","author":"Ollevier","year":"2025","journal-title":"Limnol. Oceanogr. Methods"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.5194\/essd-16-1265-2024","article-title":"Metazoan zooplankton in the Bay of Biscay: A 16-year record of individual sizes and abundances obtained using the ZooScan and ZooCAM imaging systems","volume":"16","author":"Grandremy","year":"2024","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_14","unstructured":"Kozhova, O.M., and Melnik, N.G. (1978). Instructions for Processing Plankton Samples by Counting Method, Akvakul\u2019tura Rossii. (In Russian)."},{"key":"ref_15","unstructured":"Jocher, G., and Qiu, J. (2026, February 23). Ultralytics YOLO11 (version 11.0.0). Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_16","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 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1109\/TPAMI.2021.3087709","article-title":"ArcFace: Additive Angular Margin Loss for Deep Face Recognition","volume":"44","author":"Deng","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","unstructured":"Musgrave, K., Belongie, S., and Lim, S.N. (2020). PyTorch Metric Learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TBDATA.2025.3618474","article-title":"The faiss library","volume":"12","author":"Douze","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"ref_20","first-page":"139","article-title":"One-class SVMs for document classification","volume":"2","author":"Manevitz","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"205","DOI":"10.21105\/joss.00205","article-title":"hdbscan: Hierarchical density based clustering","volume":"2","author":"McInnes","year":"2017","journal-title":"J. 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