{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T08:00:09Z","timestamp":1787644809038,"version":"build-2736575974"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32502968"],"award-info":[{"award-number":["32502968"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52302515"],"award-info":[{"award-number":["52302515"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2023A1515110691"],"award-info":[{"award-number":["2023A1515110691"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Project of State Key Laboratory of Swine and Poultry Breeding Industry","award":["ZQQZ-31"],"award-info":[{"award-number":["ZQQZ-31"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>In intensive cage rearing systems, accurate dead hen detection remains difficult due to complex environments, severe occlusion, and the high visual similarity between dead hens and live hens in a prone posture. To address these issues, this study proposes a dead hen identification method based on a Spatial-Temporal Graph Convolutional Network (STGCN). Unlike conventional static image-based approaches, the proposed method introduces temporal information to enable dynamic spatial-temporal modeling of hen health states. First, a multimodal fusion algorithm is applied to visible light and thermal infrared images to strengthen multimodal feature representation. Then, an improved YOLOv7-Pose algorithm is used to extract the skeletal keypoints of individual hens, and the ByteTrack algorithm is employed for multi-object tracking. Based on these results, spatial-temporal graph-structured data of hens are constructed by integrating spatial and temporal dimensions. Finally, a spatial-temporal graph convolution model is used to identify dead hens by learning spatial-temporal dependency features from skeleton sequences. Experimental results show that the improved YOLOv7-Pose model achieves an average precision (AP) of 92.8% in keypoint detection. Based on the constructed spatial-temporal graph data, the dead hen identification model reaches an overall classification accuracy of 99.0%, with an accuracy of 98.9% for the dead hen category. These results demonstrate that the proposed method effectively reduces interference caused by feeder occlusion and ambiguous visual features. By using dynamic spatial-temporal information, the method substantially improves robustness and accuracy of dead hen detection in complex cage rearing environments, providing a new technical route for intelligent monitoring of poultry health status.<\/jats:p>","DOI":"10.3390\/ani16030368","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:52:38Z","timestamp":1769190758000},"page":"368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dead Chicken Identification Method Based on a Spatial-Temporal Graph Convolution Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6743-0051","authenticated-orcid":false,"given":"Jikang","family":"Yang","sequence":"first","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2200-1579","authenticated-orcid":false,"given":"Chuang","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3383-1877","authenticated-orcid":false,"given":"Haikun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Energy Engineering, Guangdong Ocean University, Yangjiang 529500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4062-6713","authenticated-orcid":false,"given":"Zhenlong","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Bioscience Engineering, Katholieke Universiteit Leuven (KU LEUVEN), Kasteelpark Arenberg 30, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3324-809X","authenticated-orcid":false,"given":"Xiaohuan","family":"Chao","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"College of Animal Science, South China Agricultural University, Guangzhou 510642, China"},{"name":"State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9240-8756","authenticated-orcid":false,"given":"Cheng","family":"Fang","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China"},{"name":"College of Animal Science, South China Agricultural University, Guangzhou 510642, China"},{"name":"State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5566-4627","authenticated-orcid":false,"given":"Boyi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100287","DOI":"10.1016\/j.animal.2021.100287","article-title":"Trends for meat, milk and egg consumption for the next decades and the role played by livestock systems in the global production of proteins","volume":"15","author":"Henchion","year":"2021","journal-title":"Animal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1080\/00439339.2021.1904314","article-title":"Future demands of the poultry industry: Will we meet our commitments sustainably in developed and developing economies?","volume":"77","author":"Kleyn","year":"2021","journal-title":"World\u2019s Poult. 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