Livestock Biometrics Identification Using Computer Vision Approaches: A Review
In the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/1/102 |
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| author | Hua Meng Lina Zhang Fan Yang Lan Hai Yuxing Wei Lin Zhu Jue Zhang |
| author_facet | Hua Meng Lina Zhang Fan Yang Lan Hai Yuxing Wei Lin Zhu Jue Zhang |
| author_sort | Hua Meng |
| collection | DOAJ |
| description | In the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual growth, and prevention of theft and fraud. These advancements are pivotal to the efficient and sustainable development of the livestock industry. Recently, visual livestock biometrics have emerged as a highly promising research focus due to their non-invasive nature. This paper aims to comprehensively survey the techniques for individual livestock identification based on computer vision methods. It begins by elucidating the uniqueness of the primary biometric features of livestock, such as facial features, and their critical role in the recognition process. This review systematically overviews the data collection environments and devices used in related research, providing an analysis of the impact of different scenarios on recognition accuracy. Then, the review delves into the analysis and explication of livestock identification methods, based on extant research outcomes, with a focus on the application and trends of advanced technologies such as deep learning. We also highlight the challenges faced in this field, such as data quality and algorithmic efficiency, and introduce the baseline models and innovative solutions developed to address these issues. Finally, potential future research directions are explored, including the investigation of multimodal data fusion techniques, the construction and evaluation of large-scale benchmark datasets, and the application of multi-target tracking and identification technologies in livestock scenarios. |
| format | Article |
| id | doaj-art-a5eb504bd526433d80d3fc051b4d7145 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-a5eb504bd526433d80d3fc051b4d71452025-08-20T02:47:04ZengMDPI AGAgriculture2077-04722025-01-0115110210.3390/agriculture15010102Livestock Biometrics Identification Using Computer Vision Approaches: A ReviewHua Meng0Lina Zhang1Fan Yang2Lan Hai3Yuxing Wei4Lin Zhu5Jue Zhang6College of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaCollege of Physics and Electronic Information, Inner Mongolia Normal University, Hohhot 010022, ChinaIn the domain of animal management, the technology for individual livestock identification is in a state of continuous evolution, encompassing objectives such as precise tracking of animal activities, optimization of vaccination procedures, effective disease control, accurate recording of individual growth, and prevention of theft and fraud. These advancements are pivotal to the efficient and sustainable development of the livestock industry. Recently, visual livestock biometrics have emerged as a highly promising research focus due to their non-invasive nature. This paper aims to comprehensively survey the techniques for individual livestock identification based on computer vision methods. It begins by elucidating the uniqueness of the primary biometric features of livestock, such as facial features, and their critical role in the recognition process. This review systematically overviews the data collection environments and devices used in related research, providing an analysis of the impact of different scenarios on recognition accuracy. Then, the review delves into the analysis and explication of livestock identification methods, based on extant research outcomes, with a focus on the application and trends of advanced technologies such as deep learning. We also highlight the challenges faced in this field, such as data quality and algorithmic efficiency, and introduce the baseline models and innovative solutions developed to address these issues. Finally, potential future research directions are explored, including the investigation of multimodal data fusion techniques, the construction and evaluation of large-scale benchmark datasets, and the application of multi-target tracking and identification technologies in livestock scenarios.https://www.mdpi.com/2077-0472/15/1/102livestock individual identificationvisual biometricsenvironmentdeviceidentification methodsdeep learning |
| spellingShingle | Hua Meng Lina Zhang Fan Yang Lan Hai Yuxing Wei Lin Zhu Jue Zhang Livestock Biometrics Identification Using Computer Vision Approaches: A Review Agriculture livestock individual identification visual biometrics environment device identification methods deep learning |
| title | Livestock Biometrics Identification Using Computer Vision Approaches: A Review |
| title_full | Livestock Biometrics Identification Using Computer Vision Approaches: A Review |
| title_fullStr | Livestock Biometrics Identification Using Computer Vision Approaches: A Review |
| title_full_unstemmed | Livestock Biometrics Identification Using Computer Vision Approaches: A Review |
| title_short | Livestock Biometrics Identification Using Computer Vision Approaches: A Review |
| title_sort | livestock biometrics identification using computer vision approaches a review |
| topic | livestock individual identification visual biometrics environment device identification methods deep learning |
| url | https://www.mdpi.com/2077-0472/15/1/102 |
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