Camera-Based Sow ( Sus scrofa domesticus Erxleben) Posture Analysis and Prediction of Artificial Insemination Timing

Sookeun Song, Minseo Jo, Bong-kuk Lee, Sangkeum Lee and Hyunbean Yi ()
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Sookeun Song: Department of Computer Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Minseo Jo: Department of Computer Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Bong-kuk Lee: IT Convergence Technology Research Department, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea
Sangkeum Lee: Department of Computer Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Hyunbean Yi: Department of Computer Engineering, Hanbat National University, Daejeon 34158, Republic of Korea

Agriculture, 2025, vol. 15, issue 18, 1-13

Abstract: Determining sow ( Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems.

Keywords: estrus detection; artificial insemination; sow behavior; postural analysis; camera-based (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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