Livestock Biometrics Identification Using Computer Vision Approaches: A Review

• Published in: Agriculture (Basel) 2025-01, Vol.15 (1), p.102
• Main Authors: Meng, Hua, Zhang, Lina, Yang, Fan, Hai, Lan, Wei, Yuxing, Zhu, Lin, Zhang, Jue
• Format: Article
• Language: English
• Subjects: Accuracy; Animal husbandry; Biometrics; Cameras; Cattle; Computer vision; Data collection; Data integration; Deep learning; device; Disease control; environment; Fraud; Goats; Horses; Identification; Identification methods; Impact analysis; Industrial development; Iris; Livestock; livestock individual identification; Livestock industry; Localization; Machine learning; Marking and tracking techniques; Multiple target tracking; Neural networks; Pattern recognition; Reviews; Sheep; Software; Sustainable development; Theft; Tracking; Vaccination; visual biometrics
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture15010102

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.