Accurate identification of individual sheep is a crucial prerequisite for establishing digital sheep farms and precision livestock farming. In this study, we developed a lightweight sheep face recognition model, YOLOv7-SFR. Utilizing a face image recording channel, we efficiently collected facial images from 50 experimental sheep, resulting in a comprehensive sheep face dataset. Training results demonstrated that YOLOv7-SFR surpassed state-of-the-art lightweight sheep face recognition models, achieving a mean average precision@0.5 of 96.9%. Notably, the model size and average recognition time of YOLOv7-SFR were merely 11.3 MB and 3.6 ms, respectively. In summary, YOLOv7-SFR strikes an optimal balance between performance, model size, and recognition speed, offering promising practical applications for sheep face recognition technology. This study employs deep learning for sheep face recognition tasks, ensuring the welfare of sheep in the realm of digital agriculture and automation practices.

Read the full article in the Journal of Animal Science.