Tracking perching behavior of cage-free laying hens with deep learning technologies.

Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure that hens engage in perching behavior from an early age, as manual observation is often labor-intensive and sometimes inaccurate. The objectives of this study were to (1) develop and test a deep learning model for detecting perching behavior; and (2) evaluate the optimal model's performance on detecting perching behavior of laying hens of different ages. In this study, recent deep learning models, that is, YOLOv8s-PB, YOLOv8x-PB, YOLOv7-PB, and YOLOv7x-PB, were developed, trained and compared in detecting perching behavior in 4 CF rooms (200 hens/room). Perch height was up to 1.8 m from the litter floor and situated 1.5 m below the cameras. A total of 3,000 images were used, with each image featuring at least 1 hen perching. The models' detection accuracies and their performance across different age groups of hens were compared using 1-way ANOVA at a 5% significance level. The results showed that the YOLOv8x-PB model outperform all other models used, achieving the precision of 94.80%, recall of 95.10%, and mean average precision (mAP@0.50) of 97.60%. While all models proved over 94% detection precision. With optimal model, PB detection precision was highest (97.40%) for peaking phase followed by prelay (95.20%), grower (94.80%), developer (94.70%) and layers (92.70%) phases while the lowest detection precision (88.80%) was for starter phase. Detection performance was somewhat reduced by the overlapping of birds during perching and occlusion. Overall, the YOLOv8x-PB model was the most optimal in detecting perching behavior, proposing a valuable tool for CF producers to monitor the perching activities of laying hens automatically.