Monitoring Enrichment Block Pecking Behavior of Cage-Free Laying Hens with Deep Learning

US egg production is undergoing a transition to cage-free (CF) housing systems. This transition has increased the need for automated monitoring tools to support welfare management and reduce production costs. While CF houses allow hens to perform natural behaviors such as dust bathing and foraging, a persistent challenge is severe feather pecking. Pecking block enrichment is used as a managemental approach to control severe feather pecking. However, manual quantification of such behavior is subjective and labor-intensive. This study evaluated the performance of small and large variants of both YOLOv10 and YOLO11 models for automatic detection of enrichment block pecking behavior in CF research environment. A total of 1061 color images were used to train and evaluate the models using 70:20:10 split for training, validation, and testing. Performance was assessed using precision, recall, mean average precision at 50% intersection over union (mAP50), confusion matrices, and F1–confidence curve. All models demonstrated robust performance, with precision, recall and mAP50 values greater than 0.94. YOLO11l achieved the highest precision with 0.969 and mAP50 with 0.988, while YOLOv10s achieved the highest recall of 0.962. Evaluation on test datasets showed robust generalization capability of the model, with high confidence detections. Overall, the findings show that YOLO models provide a consistent, objective, and scalable method for automatic quantification of pecking enrichment block related pecking behavior in a CF system. It offers potential as an automated monitoring tool for poultry researchers and may support future development of tools for commercial CF system.