Advanced Deep Learning Methods for Multiple Behavior Classification of Cage-Free Laying Hens

The welfare of hens in cage-free systems is closely linked to their behaviors, such as feeding, drinking, pecking, perching, bathing, preening, and foraging. To monitor these behaviors, we developed and evaluated deep learning models based on YOLO (You Only Look Once), an advanced object detection technology known for its high accuracy, speed, and compact size. Three YOLO-based models—YOLOv5s_BH, YOLOv5x_BH, and YOLOv7_BH—were created to track and classify the behaviors of laying hens in cage-free environments. A dataset comprising 1500 training images, 500 validation images, and 50 test images was used to train and validate the models. The models successfully detected poultry behaviors in test images with bounding boxes and objectness scores ranging from 0 to 1. Among the models, YOLOv5s_BH demonstrated superior performance, achieving a precision of 78.1%, surpassing YOLOv5x_BH and YOLOv7_BH by 1.9% and 2.2%, respectively. It also achieved a recall of 71.7%, outperforming YOLOv5x_BH and YOLOv7_BH by 1.9% and 2.8%, respectively. Additionally, YOLOv5s_BH recorded a mean average precision (mAP) of 74.6%, exceeding YOLOv5x_BH by 2.6% and YOLOv7_BH by 9%. While all models demonstrated high detection precision, their performance was influenced by factors such as stocking density, varying light conditions, and obstructions from equipment like drinking lines, perches, and feeders. This study highlights the potential for the automated monitoring of poultry behaviors in cage-free systems, offering valuable insights for producers.