Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools

SIMPLE SUMMARY: Precision livestock farming (PLF) techniques facilitate automated, continuous, and real-time monitoring of animal behaviour and physiological responses. They also have the potential to improve animal welfare by providing a continuous picture of welfare states, thus enabling fast actions that benefit the flock. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary—a complex environment—by comparing the performance of two machine learning (YOLO, You Only Look Once) models. The results of the study revealed that the two models had a similar performance; however, while PLF was successful in evaluating the distribution of hens on the floor and predicting undesired events, such as smothering due to overcrowding, it failed to identify the occurrence of comfort behaviours, such as dust bathing, which are part of the evaluation of hen welfare. ABSTRACT: Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4-tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust-bathing hens was poor (28.2% in the YOLOv4-tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dust-bathing hens.