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Multi Behavior Detection and Statistical Method for Beef Cattle Based on Local Perception
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Fang WANG1, 2, Xingyu LIU1, 2, Lisheng REN1, 2, Chenfu FU1, 2, Huixuan JIA1, 2
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 603 - 614
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 603-614
• Agricultural Informatization Engineering •
Multi Behavior Detection and Statistical Method for Beef Cattle Based on Local Perception
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Fang WANG1, 2, Xingyu LIU1, 2, Lisheng REN1, 2, Chenfu FU1, 2, Huixuan JIA1, 2
Affiliations
  • 1.College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
  • 2.Key Laboratory of Agricultural Big Data of Hebei Province, Baoding 071001, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.055
Outline
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Given the current research on beef cattle behavior, which mainly focused on basic behavior recognition and lacked local perception of complex behaviors, the identification of fine behaviors under basic behaviors of beef cattle was researched. A multi-behavior detection and statistical method based on YOLO v8 for beef cattle was proposed. Cattle behavior images were collected by cameras to build a comprehensive dataset that included fundamental behaviors such as standing, lying down, feeding, and drinking, as well as fine behaviors like licking, walking, searching, and tail flicking. YOLO v8n-P2 was selected as the basic model to enhance the ability of the model to detect calves; the feature extraction structure of C2F-PPA was designed; the YOLO v8 detection head was improved by interactive strategy, and the TDADH was constructed. MPDIoU loss function was used to address limitations associated with CIoU. Subsequently, statistical analysis of cattle behaviors based on detection results throughout the day was conducted; these results were visually displayed for various occlusion environments. The experimental results showed that the PTPM-YOLO v8n model achieved precision rate of 91.0%, a recall rate of 87.9%, and a mAP@0.5 score of 94.3% in recognizing all eight behaviors tested. Compared with the original model YOLO v8n, the mAP@0.5 of PTPM-YOLO v8n was increased by 3.0 percentage points, and the parameter number was decreased by 21.9%. which identified all behaviors and basic behaviors, the mAP@0.5 was increased by 3.0 and 2.4 percentage points, respectively. The method presented can accurately identify fine behaviors of beef cattle under farming conditions, providing a reference for multi-behavior monitoring of beef cattle.

beef cattle  /  object detection  /  multi behavior recognition  /  behavioral statistics  /  YOLO v8
王芳, 刘星宇, 任力生, 付辰伏, 贾惠煊. 基于局部感知的肉牛多行为检测与统计方法. 农业机械学报, 2025 , 56 (12) : 603 -614 . DOI: 10.6041/j.issn.1000-1298.2025.12.055
Fang WANG, Xingyu LIU, Lisheng REN, Chenfu FU, Huixuan JIA. Multi Behavior Detection and Statistical Method for Beef Cattle Based on Local Perception[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 603 -614 . DOI: 10.6041/j.issn.1000-1298.2025.12.055
Year 2025 volume 56 Issue 12
167
74
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doi: 10.6041/j.issn.1000-1298.2025.12.055
  • Receive Date:2024-08-10
  • Online Date:2026-09-30
  • Published:2025-12-25
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  • Received:2024-08-10
  • Revised:2024-10-13
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    1.College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
    2.Key Laboratory of Agricultural Big Data of Hebei Province, Baoding 071001, 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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