Deep learning based landmark detection for measuring hock and knee angles in sows

This paper presents a visual deep learning approach to automatically determine hock and knee angles from sow images. Lameness is the second largest reason for culling of breeding herd females and relies on human observers to provide visual scoring for detection which can be slow, subjective, and inconsistent. A deep learning model classified and detected ten and two key body landmarks from the side and rear profile images, respectively (mean average precision = 0.94). Trigonometric-based formulae were derived to calculate hock and knee angles using the features extracted from the imagery. Automated angle measurements were compared with manual results from each image (average root mean square error [RMSE] = 4.13°), where all correlation slopes (average R ² = 0.84) were statistically different from zero (P < 0.05); all automated measurements were in statistical agreement with manually collected measurements using the Bland-Altman procedure. This approach will be of interest to animal geneticists, scientists, and practitioners for obtaining objective angle measurements that can be factored into gilt replacement criteria to optimize sow breeding units.