Behavioral Welfare Monitoring in Laying Hens: From Ethology to Artificial Intelligence—A Narrative Review

Automated monitoring of welfare-relevant behavior in laying hens (Gallus gallus domesticus) has advanced with developments in computer vision, deep learning, and precision livestock farming (PLF). This narrative review integrates the ethological basis of welfare indicators with the development and readiness of monitoring technologies. It distinguishes routinely expressed diagnostic behaviors, including preening, locomotion, dustbathing, feeding, drinking, and nesting, from high-priority welfare risks such as aggression, piling, feather pecking, and inactivity or prostration. The review traces the progression from manual ethograms to semi-automated tools and recent artificial intelligence (AI) applications. These include You Only Look Once (YOLO)-based detection, multi-object tracking with BoT-SORT (a robust association-based tracking algorithm), pose estimation, and multimodal sensor fusion. Application-specific Technology Readiness Levels (TRL 1–9) indicate that most behavior-analysis systems remain at TRL 4–6. Several technologies extend into TRL 6–8, whereas few established systems reach TRL 9. Persistent barriers include domain shift under production conditions, annotation costs, inconsistent validation protocols, and limited economic accessibility. By linking welfare relevance, evaluation level, and deployment evidence, this review identifies priorities for scalable and actionable monitoring in commercial laying-hen production.