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Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning

文献类型： 外文期刊

作者： Lei, Kaidong 1 ; Li, Bugao 1 ; Zhong, Shan 1 ; Yang, Hua 1 ; Wang, Hao 2 ; Tang, Xiangfang 3 ; Xiong, Benhai 3 ;

作者机构： 1.Shanxi Agr Univ, Coll Informat Sci & Engn, Taiyuan 030800, Peoples R China

2.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

3.Chinese Acad Agr Sci, Inst Anim Sci, State Key Lab Anim Nutr, Beijing 100193, Peoples R China

关键词： 3D-CNN; video processing; sow cycle; behavior recognition; animal welfare; precision livestock farming

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 9 期

页码：

收录情况： SCI

摘要： Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep learning, more technologies are being integrated into smart agriculture. Intelligent large-scale pig farming has become an effective means to improve sow quality and productivity, with behavior recognition technology playing a crucial role in intelligent pig farming. Specifically, monitoring sow behavior enables an effective assessment of health conditions and welfare levels, ensuring efficient and healthy sow production. This study constructs a 3D-CNN model based on video data from the sow estrus cycle, achieving analysis of SOB, SOC, SOS, and SOW behaviors. In typical behavior classification, the model attains accuracy, recall, and F1-score values of (1.00, 0.90, 0.95; 0.96, 0.98, 0.97; 1.00, 0.96, 0.98; 0.86, 1.00, 0.93), respectively. Additionally, under conditions of multi-pig interference and non-specifically labeled data, the accuracy, recall, and F1-scores for the semantic recognition of SOB, SOC, SOS, and SOW behaviors based on the 3D-CNN model are (1.00, 0.90, 0.95; 0.89, 0.89, 0.89; 0.91, 1.00, 0.95; 1.00, 1.00, 1.00), respectively. These findings provide key technical support for establishing the classification and semantic recognition of typical sow behaviors during the estrus cycle, while also offering a practical solution for rapid video-based behavior detection and welfare monitoring in precision livestock farming.

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