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

Kaidong Lei, Bugao Li, Shan Zhong, Hua Yang, Hao Wang, Xiangfang Tang () and Benhai Xiong
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Kaidong Lei: College of Information Science and Engineering, Shanxi Agricultural University, Taiyuan 030800, China
Bugao Li: College of Information Science and Engineering, Shanxi Agricultural University, Taiyuan 030800, China
Shan Zhong: College of Information Science and Engineering, Shanxi Agricultural University, Taiyuan 030800, China
Hua Yang: College of Information Science and Engineering, Shanxi Agricultural University, Taiyuan 030800, China
Hao Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Xiangfang Tang: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Benhai Xiong: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China

Agriculture, 2025, vol. 15, issue 9, 1-13

Abstract: 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.

Keywords: 3D-CNN; video processing; sow cycle; behavior recognition; animal welfare; precision livestock farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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