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Individual Identification Method of Simmental Cattle Based on Improved YOLO v8 Keypoint Detection
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Zhenjiang TIAN1, Cheng HAN1, Chao ZHANG1, Yueying LI1, Fudong YU2, Xinhui ZHONG2
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 591 - 602
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 591-602
• Agricultural Informatization Engineering •
Individual Identification Method of Simmental Cattle Based on Improved YOLO v8 Keypoint Detection
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Zhenjiang TIAN1, Cheng HAN1, Chao ZHANG1, Yueying LI1, Fudong YU2, Xinhui ZHONG2
Affiliations
  • 1.School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130012, China
  • 2.Jilin Sino Agri Sunshine Data Co., Ltd., Changchun 130022, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.054
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With the intelligent development of animal husbandry, computer-vision-based individual cattle recognition is playing an increasingly important role in modern cattle farming. However, in practical applications, the rapid changes in the angles of cattle faces, frequent fluctuations in the number of cattle, and limitations in model recognition speed severely affect the accuracy and efficiency of recognition. To address these challenges, an individual recognition method for Simmental cattle was proposed based on improved YOLO v8 keypoint detection. The method consisted of two stages: in the cattle face detection stage, the improved YOLO v8 model automatically detected keypoints in cattle face images and performed correction alignment and background cropping to make the angles of the cattle face images more consistent. In the cattle face recognition stage, depthwise separable convolution was firstly introduced to reduce the number of parameters, and then residual connections and attention mechanisms were utilized to enhance the model's feature extraction capabilities, thereby improving recognition accuracy and speed. To validate the effectiveness of the method, a dataset of cattle face images containing 159 Simmental cattle was constructed. Experimental results showed that the proposed model had significant improvements in the number of parameters and inference speed, with processing time for a single image reduced to within 39 ms and a recognition accuracy of 95.8%, an average improvement of 4.8 percentage points compared with that of other models. Additionally, the proposed method supported real-time updates to the cattle database, maintaining high accuracy even after adding new cattle face images.

cow face recognition  /  computer vision  /  YOLO v8  /  key point detection
田振江, 韩成, 张超, 李月莹, 于福东, 钟馨慧. 基于改进YOLO v8关键点检测的西门塔尔牛只个体识别方法. 农业机械学报, 2025 , 56 (12) : 591 -602 . DOI: 10.6041/j.issn.1000-1298.2025.12.054
Zhenjiang TIAN, Cheng HAN, Chao ZHANG, Yueying LI, Fudong YU, Xinhui ZHONG. Individual Identification Method of Simmental Cattle Based on Improved YOLO v8 Keypoint Detection[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 591 -602 . DOI: 10.6041/j.issn.1000-1298.2025.12.054
Year 2025 volume 56 Issue 12
159
73
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Article Info
doi: 10.6041/j.issn.1000-1298.2025.12.054
  • Receive Date:2024-08-12
  • Online Date:2026-09-30
  • Published:2025-12-25
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  • Received:2024-08-12
  • Revised:2024-10-18
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Affiliations
    1.School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130012, China
    2.Jilin Sino Agri Sunshine Data Co., Ltd., Changchun 130022, China
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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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