{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:34:08Z","timestamp":1777390448307,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JKA","award":["2023M-425"],"award-info":[{"award-number":["2023M-425"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow\u2019s body, with a specific emphasis on the back region\u2019s maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow\u2019s depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow\u2019s backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management.<\/jats:p>","DOI":"10.3390\/jimaging10030067","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T05:44:06Z","timestamp":1709876646000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification"],"prefix":"10.3390","volume":"10","author":[{"given":"San Chain","family":"Tun","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan"}]},{"given":"Tsubasa","family":"Onizuka","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-2984","authenticated-orcid":false,"given":"Pyke","family":"Tin","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan"}]},{"given":"Masaru","family":"Aikawa","sequence":"additional","affiliation":[{"name":"Organization for Learning and Student Development, University of Miyazaki, Miyazaki 889-2192, Japan"}]},{"given":"Ikuo","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Sumiyoshi Livestock Science Station, Field Science Center, Faculty of Agriculture, University of Miyazaki, Miyzaki 889-2192, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3435-2197","authenticated-orcid":false,"given":"Thi Thi","family":"Zin","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"838","DOI":"10.3390\/ani5030387","article-title":"Lameness detection in dairy cows: Part 1. 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