Artificial Intelligence Assisted Selection Strategies in Sheep: Linking Reproductive Traits with Behavioral Indicators

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning algorithms, provide new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes current research on the use of AI-powered behavioral monitoring tools to enhance selection accuracy and breeding outcomes. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring of individual animals, allowing for the early detection of reproductive events and maternal performance. We also present a conceptual model, ReproBehaviorNet, that maps age and sex specific behaviors to bio-logical processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. This approach not only improves breeding pre-cision and genetic progress but also contributes to better animal welfare and sustainable management. Our findings underscore the importance of interdisciplinary collaboration and data-driven tools in the evolution of livestock selection strategies.