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Research Progress in Livestock and Poultry Breed Identification Using Machine Learning
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Danni QIU1, Yipeng LI1, Shanghui ZHAO1, Ruiqi WANG1, Xiaohong LIU2, Xiaotian QIU3, Xiaolong YUAN1
Acta Veterinaria et Zootechnica Sinica | 2026, 57(2) : 582 - 594
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Acta Veterinaria et Zootechnica Sinica | 2026, 57(2): 582-594
• REVIEW •
Research Progress in Livestock and Poultry Breed Identification Using Machine Learning
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Danni QIU1, Yipeng LI1, Shanghui ZHAO1, Ruiqi WANG1, Xiaohong LIU2, Xiaotian QIU3, Xiaolong YUAN1
Affiliations
  • 1.National Key Laboratory of Swine and Poultry Breeding，National Engineering Research Center for Breeding Swine Industry，Guangdong Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding，College of Animal Science，South China Agricultural University，Guangzhou 510642，China
  • 2.School of Life Sciences，Sun Yat-sen University，Guangzhou 510275，China
  • 3.National Animal Husbandry Service，Beijing 100125，China
Published: 2026-02-23 doi: 10.11843/j.issn.0366-6964.2026.02.002
Outline
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Breed identification is fundamental to livestock conservation and plays a vital role in genetic resource management， breed purity assessment， breeding strategy implementation， product traceability， supply chain optimization， and ensuring food and biosafety. This review summarizes both traditional and modern methods for breed identification of common livestock and poultry （pig， cattle， sheep， dog， chicken）， and systematically reviews recent advances in machine learning-based breed identification research.Traditional methods， such as morphological assessment and physicochemical indicator analysis in animals，are increasingly inadequate for large-scale breed identification due to their limited accuracy，operational complexity，and time-consuming nature. With advancements in intelligent animal husbandry and high-throughput omics technologies， machine learning-based approaches have gained wide application. This paper reviews major achievements in breed identification using machine learning， analyzes the strengths and limitations of various algorithms， and highlights challenges in practical use. It offers insights for future development and supports molecular breeding and precision livestock management.

machine learning  /  breed identification  /  livestock and poultry  /  SNP  /  image recognition  /  omics  /  intelligent animal husbandry
邱丹妮, 李以蓬, 赵尚辉, 王瑞旗, 刘小红, 邱小田, 袁晓龙. 利用机器学习开展畜禽品种鉴定的研究进展. 畜牧兽医学报, 2026 , 57 (2) : 582 -594 . DOI: 10.11843/j.issn.0366-6964.2026.02.002
Danni QIU, Yipeng LI, Shanghui ZHAO, Ruiqi WANG, Xiaohong LIU, Xiaotian QIU, Xiaolong YUAN. Research Progress in Livestock and Poultry Breed Identification Using Machine Learning[J]. Acta Veterinaria et Zootechnica Sinica, 2026 , 57 (2) : 582 -594 . DOI: 10.11843/j.issn.0366-6964.2026.02.002
Appendix
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Year 2026 volume 57 Issue 2
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Article Info
doi: 10.11843/j.issn.0366-6964.2026.02.002
  • Receive Date：2025-07-03
  • Online Date：2026-09-16
  • Published：2026-02-23
Article Data
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History
  • Received：2025-07-03
Funding
Affiliations
    1.National Key Laboratory of Swine and Poultry Breeding，National Engineering Research Center for Breeding Swine Industry，Guangdong Provincial Key Laboratory of Agricultural Animal Genomics and Molecular Breeding，College of Animal Science，South China Agricultural University，Guangzhou 510642，China
    2.School of Life Sciences，Sun Yat-sen University，Guangzhou 510275，China
    3.National Animal Husbandry Service，Beijing 100125，China

Corresponding:

YUAN Xiaolong， E-mail：
QIU Xiaotian， E-mail：
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Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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