{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:42:19Z","timestamp":1792158139890,"version":"4.4.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Malaysian Higher Education FRGS","award":["USMKI.R503-KR-FRG001-0006712190-K134.6712190"],"award-info":[{"award-number":["USMKI.R503-KR-FRG001-0006712190-K134.6712190"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Non-invasive monitoring of small mammal populations is critical for both biodiversity conservation and integrated pest management, particularly in agroecosystems. Barn owl (\n                    <jats:italic>Tyto alba<\/jats:italic>\n                    ) pellet analysis has long served as a valuable tool for inferring prey abundance, yet conventional bone classification is labour-intensive and requires specialized expertise. Here, we introduce a deep learning framework that automates the detection and classification of rodent bone fragments from owl pellets using the YOLOv12 object detection architecture. A dataset comprising 978 annotated images, encompassing skull, femur, mandible, and pubis bones, was used to train and validate the model, achieving high detection performance (precision\u2009=\u20090.90, recall\u2009=\u20090.90, mAP@0.5\u2009=\u20090.984, F1-score\u2009=\u20090.97). The model demonstrated strong generalization across samples from Malaysia and Indonesia. We further developed a Python-based inference script to estimate rodent abundance using skull and paired bone counts. This AI-assisted workflow reduces human error, increases processing throughput, and enables scalable rodent monitoring. By enhancing ecological inference from pellet studies, our approach supports timely biodiversity assessments and pest surveillance strategies across diverse landscapes.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-15906-9","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T05:37:01Z","timestamp":1755063421000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A deep learning framework for bone fragment classification in owl pellets using YOLOv12"],"prefix":"10.1038","volume":"15","author":[{"given":"Nik","family":"Fadzly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lay Wai","family":"Kean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siti Nuramaliati","family":"Prijono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rini","family":"Rachmatika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siti","family":"Zulaika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Nasir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasber","family":"Salim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"issue":"4","key":"15906_CR1","doi-asserted-by":"publisher","first-page":"627","DOI":"10.3390\/conservation4040039","volume":"4","author":"V Bontzorlos","year":"2024","unstructured":"Bontzorlos, V. et al. Barn owls as a Nature-Based solution for pest control: A multinational initiative around the mediterranean and other regions. Conservation 4 (4), 627\u2013656 (2024).","journal-title":"Conservation"},{"key":"15906_CR2","doi-asserted-by":"publisher","first-page":"139","DOI":"10.34080\/os.v31.23108","volume":"31","author":"P Jim\u00e9nez-N\u00e1jar","year":"2021","unstructured":"Jim\u00e9nez-N\u00e1jar, P., Garrido-Bautista, J., Tarifa, R., Rivas, J. M. & Moreno-Rueda, G. Diet of sympatric barn owls (Tyto alba) and Short-eared owls (Asio flammeus) in an agricultural landscape in south-east Spain. Ornis Svecica. 31, 139\u2013150 (2021).","journal-title":"Ornis Svecica"},{"key":"15906_CR3","doi-asserted-by":"publisher","first-page":"116","DOI":"10.18196\/pt.v9i2.10893","volume":"2","author":"S Sudarmaji","year":"2021","unstructured":"Sudarmaji, S. et al. The occupancy of barn Owl in the artificial nest box to control rice field rat in Yogyakarta Indonesia. Planta Tropika. 2, 116\u2013125 (2021).","journal-title":"Planta Tropika"},{"issue":"1","key":"15906_CR4","doi-asserted-by":"publisher","first-page":"juz025","DOI":"10.1093\/jue\/juz025","volume":"6","author":"S Saufi","year":"2020","unstructured":"Saufi, S. et al. Diet composition of introduced barn owls (Tyto Alba javanica) in urban area in comparison with agriculture settings. J. Urban Ecol. 6 (1), juz025 (2020).","journal-title":"J. Urban Ecol."},{"issue":"1","key":"15906_CR5","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1111\/2041-210X.12454","volume":"7","author":"LM Heisler","year":"2016","unstructured":"Heisler, L. M., Somers, C. M. & Poulin, R. G. Owl pellets: a more effective alternative to conventional trapping for broad-scale studies of small mammal communities. Methods Ecol. Evol. 7 (1), 96\u2013103 (2016).","journal-title":"Methods Ecol. Evol."},{"key":"15906_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.15407\/TU2404","volume":"24","author":"M Drebet","year":"2022","unstructured":"Drebet, M. Monitoring of the mammal fauna by studying Owl pellets: A case of small mammals in protected areas of podillia. Theriol. Ukr. 24, 16\u201327 (2022).","journal-title":"Theriol. Ukr."},{"issue":"supplement","key":"15906_CR7","first-page":"39","volume":"135","author":"N Avenant","year":"2005","unstructured":"Avenant, N. Barn Owl pellets: a useful tool for monitoring small mammal communities. Belg. J. Zool. 135 (supplement), 39\u201343 (2005).","journal-title":"Belg. J. Zool."},{"issue":"1","key":"15906_CR8","doi-asserted-by":"publisher","first-page":"18862","DOI":"10.1038\/s41598-020-75994-7","volume":"10","author":"M Dom\u00ednguez-Rodrigo","year":"2020","unstructured":"Dom\u00ednguez-Rodrigo, M. et al. Artificial intelligence provides greater accuracy in the classification of modern and ancient bone surface modifications. Sci. Rep. 10 (1), 18862 (2020).","journal-title":"Sci. Rep."},{"key":"15906_CR9","doi-asserted-by":"crossref","unstructured":"Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences. ;115(25):E5716-E25. (2018).","DOI":"10.1073\/pnas.1719367115"},{"issue":"4","key":"15906_CR10","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1111\/2041-210X.13120","volume":"10","author":"MA Tabak","year":"2019","unstructured":"Tabak, M. A. et al. Machine learning to classify animal species in camera trap images: applications in ecology. Methods Ecol. Evol. 10 (4), 585\u2013590 (2019).","journal-title":"Methods Ecol. 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(eds) CutMix: Regularization strategy to train strong classifiers with localizable features. 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