A deep learning framework for bone fragment classification in owl pellets using YOLOv12
Abstract Non-invasive monitoring of small mammal populations is critical for both biodiversity conservation and integrated pest management, particularly in agroecosystems. Barn owl (Tyto alba) pellet analysis has long served as a valuable tool for inferring prey abundance, yet conventional bone clas...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15906-9 |
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| author | Nik Fadzly Lay Wai Kean Siti Nuramaliati Prijono Rini Rachmatika Siti Zulaika Mohd Nasir Hasber Salim |
| author_facet | Nik Fadzly Lay Wai Kean Siti Nuramaliati Prijono Rini Rachmatika Siti Zulaika Mohd Nasir Hasber Salim |
| author_sort | Nik Fadzly |
| collection | DOAJ |
| description | Abstract Non-invasive monitoring of small mammal populations is critical for both biodiversity conservation and integrated pest management, particularly in agroecosystems. Barn owl (Tyto alba) 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 = 0.90, recall = 0.90, mAP@0.5 = 0.984, F1-score = 0.97). 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. |
| format | Article |
| id | doaj-art-daae98eeb8f74d8b9ad38b86b0adcf47 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-daae98eeb8f74d8b9ad38b86b0adcf472025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-08-011511910.1038/s41598-025-15906-9A deep learning framework for bone fragment classification in owl pellets using YOLOv12Nik Fadzly0Lay Wai Kean1Siti Nuramaliati Prijono2Rini Rachmatika3Siti Zulaika4Mohd Nasir5Hasber Salim6School of Biological Sciences, Universiti Sains Malaysia, USMSchool of Biological Sciences, Universiti Sains Malaysia, USMApplied Zoology Research Center, National Research and Innovation Agency (BRIN)Applied Zoology Research Center, National Research and Innovation Agency (BRIN)School of Biological Sciences, Universiti Sains Malaysia, USMSchool of Biological Sciences, Universiti Sains Malaysia, USMSchool of Biological Sciences, Universiti Sains Malaysia, USMAbstract Non-invasive monitoring of small mammal populations is critical for both biodiversity conservation and integrated pest management, particularly in agroecosystems. Barn owl (Tyto alba) 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 = 0.90, recall = 0.90, mAP@0.5 = 0.984, F1-score = 0.97). 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.https://doi.org/10.1038/s41598-025-15906-9Artificial intelligenceOwl pelletsBone classificationMachine learningObject detection |
| spellingShingle | Nik Fadzly Lay Wai Kean Siti Nuramaliati Prijono Rini Rachmatika Siti Zulaika Mohd Nasir Hasber Salim A deep learning framework for bone fragment classification in owl pellets using YOLOv12 Scientific Reports Artificial intelligence Owl pellets Bone classification Machine learning Object detection |
| title | A deep learning framework for bone fragment classification in owl pellets using YOLOv12 |
| title_full | A deep learning framework for bone fragment classification in owl pellets using YOLOv12 |
| title_fullStr | A deep learning framework for bone fragment classification in owl pellets using YOLOv12 |
| title_full_unstemmed | A deep learning framework for bone fragment classification in owl pellets using YOLOv12 |
| title_short | A deep learning framework for bone fragment classification in owl pellets using YOLOv12 |
| title_sort | deep learning framework for bone fragment classification in owl pellets using yolov12 |
| topic | Artificial intelligence Owl pellets Bone classification Machine learning Object detection |
| url | https://doi.org/10.1038/s41598-025-15906-9 |
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