Livestock Biometrics Identification Using Computer Vision Approaches: A Review

被引:0
|
作者
Meng, Hua [1 ]
Zhang, Lina [1 ]
Yang, Fan [1 ]
Hai, Lan [1 ]
Wei, Yuxing [1 ]
Zhu, Lin [1 ]
Zhang, Jue [1 ]
机构
[1] Inner Mongolia Normal Univ, Coll Phys & Elect Informat, Hohhot 010022, Peoples R China
来源
AGRICULTURE-BASEL | 2025年 / 15卷 / 01期
基金
中国国家自然科学基金;
关键词
livestock individual identification; visual biometrics; environment; device; identification methods; deep learning; INDIVIDUAL IDENTIFICATION; FACE RECOGNITION; CATTLE; IRIS; TECHNOLOGY;
D O I
10.3390/agriculture15010102
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页数:31
相关论文
共 50 条
  • [1] Identification and Recognition of Animals from Biometric Markers Using Computer Vision Approaches: A Review
    Cihan, Pinar
    Saygili, Ahmet
    Ozmen, Nihat Eren
    Akyuzlu, Muhammed
    KAFKAS UNIVERSITESI VETERINER FAKULTESI DERGISI, 2023, 29 (06) : 581 - 593
  • [2] Ear biometrics in computer vision
    Burge, M
    Burger, W
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS, 2000, : 822 - 826
  • [3] A review of deep learning algorithms for computer vision systems in livestock
    Oliveira, Dario Augusto Borges
    Pereira, Luiz Gustavo Ribeiro
    Bresolin, Tiago
    Ferreira, Rafael Ehrich Pontes
    Dorea, Joao Ricardo Reboucas
    LIVESTOCK SCIENCE, 2021, 253
  • [4] Possible Approaches to Arecanut Sorting/Grading using Computer Vision: A Brief Review
    Bharadwaj, N. K.
    Dinesh, R.
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2017, : 1007 - 1014
  • [5] Guest Editorial: Computer Vision for Animal Biometrics
    Burghardt, Tilo
    Fisher, Robert B.
    Ravela, Sai
    IET COMPUTER VISION, 2018, 12 (02) : 119 - 120
  • [6] Violence Detection Using Computer Vision Approaches
    Talha, Khalid Raihan
    Bandapadya, Koushik
    Khan, Mohammad Monirujjaman
    2022 IEEE WORLD AI IOT CONGRESS (AIIOT), 2022, : 544 - 550
  • [7] Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review
    Waeldchen, Jana
    Maeder, Patrick
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2018, 25 (02) : 507 - 543
  • [8] Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review
    Jana Wäldchen
    Patrick Mäder
    Archives of Computational Methods in Engineering, 2018, 25 : 507 - 543
  • [9] Identification of tea varieties using computer vision
    School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
    不详
    Trans. ASABE, 2008, 2 (623-628):
  • [10] Identification of tea varieties using computer vision
    Chen, Q.
    Zhao, J.
    Cai, J.
    TRANSACTIONS OF THE ASABE, 2008, 51 (02): : 623 - 628