Research trends in livestock facial identification: a review
This review examines the application of video processing and convolutional neural network (CNN)-based deep learning for animal face recognition, identification, and re-identification. These technologies are essential for precision livestock farming, addressing challenges in production efficiency, an...
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| Format: | Article |
| Language: | English |
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Korean Society of Animal Sciences and Technology
2025-01-01
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| Series: | Journal of Animal Science and Technology |
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| Online Access: | http://www.ejast.org/archive/view_article?doi=10.5187/jast.2025.e4 |
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| _version_ | 1850087145489825792 |
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| author | Mun-Hye Kang Sang-Hyon Oh |
| author_facet | Mun-Hye Kang Sang-Hyon Oh |
| author_sort | Mun-Hye Kang |
| collection | DOAJ |
| description | This review examines the application of video processing and convolutional neural network (CNN)-based deep learning for animal face recognition, identification, and re-identification. These technologies are essential for precision livestock farming, addressing challenges in production efficiency, animal welfare, and environmental impact. With advancements in computer technology, livestock monitoring systems have evolved into sensor-based contact methods and video-based non-contact methods. Recent developments in deep learning enable the continuous analysis of accumulated data, automating the monitoring of animal conditions. By integrating video processing with CNN-based deep learning, it is possible to estimate growth, identify individuals, and monitor behavior more effectively. These advancements enhance livestock management systems, leading to improved animal welfare, production outcomes, and sustainability in farming practices. |
| format | Article |
| id | doaj-art-8e2d9539eaec4872a54643967f65d1f7 |
| institution | DOAJ |
| issn | 2672-0191 2055-0391 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Korean Society of Animal Sciences and Technology |
| record_format | Article |
| series | Journal of Animal Science and Technology |
| spelling | doaj-art-8e2d9539eaec4872a54643967f65d1f72025-08-20T02:43:16ZengKorean Society of Animal Sciences and TechnologyJournal of Animal Science and Technology2672-01912055-03912025-01-01671435510.5187/jast.2025.e4Research trends in livestock facial identification: a reviewMun-Hye Kang0Sang-Hyon Oh1Division of Aerospace and Software Engineering, Gyeongsang National University, Jinju 52828, KoreaDivision of Animal Science, Gyeongsang National University, Jinju 52828, KoreaThis review examines the application of video processing and convolutional neural network (CNN)-based deep learning for animal face recognition, identification, and re-identification. These technologies are essential for precision livestock farming, addressing challenges in production efficiency, animal welfare, and environmental impact. With advancements in computer technology, livestock monitoring systems have evolved into sensor-based contact methods and video-based non-contact methods. Recent developments in deep learning enable the continuous analysis of accumulated data, automating the monitoring of animal conditions. By integrating video processing with CNN-based deep learning, it is possible to estimate growth, identify individuals, and monitor behavior more effectively. These advancements enhance livestock management systems, leading to improved animal welfare, production outcomes, and sustainability in farming practices. http://www.ejast.org/archive/view_article?doi=10.5187/jast.2025.e4LivestockRecognitionIdentificationRe-identificationConvolutional neural networkDeep learning |
| spellingShingle | Mun-Hye Kang Sang-Hyon Oh Research trends in livestock facial identification: a review Journal of Animal Science and Technology Livestock Recognition Identification Re-identification Convolutional neural network Deep learning |
| title | Research trends in livestock facial identification: a review |
| title_full | Research trends in livestock facial identification: a review |
| title_fullStr | Research trends in livestock facial identification: a review |
| title_full_unstemmed | Research trends in livestock facial identification: a review |
| title_short | Research trends in livestock facial identification: a review |
| title_sort | research trends in livestock facial identification a review |
| topic | Livestock Recognition Identification Re-identification Convolutional neural network Deep learning |
| url | http://www.ejast.org/archive/view_article?doi=10.5187/jast.2025.e4 |
| work_keys_str_mv | AT munhyekang researchtrendsinlivestockfacialidentificationareview AT sanghyonoh researchtrendsinlivestockfacialidentificationareview |