Work

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

Year: 2025

Type: article

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.

Source: Agriculture

DOI: 10.3390/agriculture15181918

Authors: Sookeun Song, Minseo Jo, Bong-kuk Lee, Sangkeum Lee, Hyunbean Yi

Institutions: Hanbat National University, Electronics and Telecommunications Research Institute

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137