Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models - Venkat U.C. Bodempudi; Guoming Li; J. Hunter Mason; Jeanna L. Wilson; Tianming Liu; Khaled M. Rasheed (Poultry Science)
Mating behaviors are crucial for bird welfare, reproduction, and productivity in breeding flocks. During mating, a rooster mounts a hen, which may result in the hen overlapping or disappearing from top-view of a vision system. The objective of this research was to develop Deep learning models (DLM) to identify mating behavior based on bird count changes and bio-characteristics of mating. Twenty broiler breeder hens and 2-3 roosters (56 weeks) of the Ross 708 breed were monitored in four experimental pens. The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. Pretrained models of object detection (You Only Look Once Version 7 and 8, YOLOv7 and YOLOv8), tracking (YOLOv7 or YOLOv8 with Deep Simple Online Real-time Tracking (SORT), StrongSORT, and ByteTrack), and segmentation (Segment Anything Model2 (SAM2), YOLOv8-segmentation, Track Anything) were comparatively evaluated for bird detection, and YOLOv8l object detection model was selected due to balanced performance in processing speed (8 seconds per frame) and accuracy (75 % Mean Average Precision (mAP)). With custom training, the best performance of detecting broiler breeders via YOLOv8l was over 0.939 precision, recall, mAP50, mAP95, and F1 score for training and 0.95 positive and negative predicted values for testing. After comparing 24 scenarios of mating duration and 32 scenarios of time interval, a mating duration of 3-9 seconds and the time intervals of T-3 to T+12 seconds based on manual observation were incorporated into the framework to filter out unnecessary data and retain keyframes for further processing, significantly reducing the processing speed by a factor of 10. The optimized framework was effectively able to detect the birds and identify the mating behavior with 0.92 accuracy compared to other YOLO detection plus logic frameworks. Mating event identification via the developed DLM framework fluctuated among different time of a day and bird ages due to bird overlapping, gathering densities, and occlusions. By automating this process, breeders can efficiently monitor and analyze mating behaviors, facilitating timely interventions and adjustments in housing and management practices to optimize broiler breeder fertility, genetics, and overall productivity.