Automated measurement of dairy cows body size via 3D point cloud data analysis

Abstract

Body measurement plays an important role in the breeding and production of dairy cows. Body measurement is mainly done manually, which is laborious, imprecise, and prone to stressful behavior in animals. A portable non-contact 3D measurement system of dairy cow body using smartphones was proposed in this study. Reconstruction of 3D point clouds scene containing the cow through a Structure-from-Motion (SfM) photogrammetry, the RANdom SAmple Consensus (RANSAC), and the Euclidean clustering were used to segment the scene and extract cow’s point clouds. Aiming to solve the holes in the cow point cloud caused by the barrier and legs occlusion, a completion method based on the spline curve with a smoothing factor was proposed. The automatic measurement of body size was realized through morphological features. The accuracy of the proposed system was evaluated in a commercial dairy farm. Compared with mesh-based method, the proposed point cloud hole completion method reduced the maximum relative errors of chest girth and chest width by 2.22% and 2.18%. Under different cow postures, the average relative error between automatic body size measurement algorithm and manual calibration was less than 4.67%. The average relative error between the measured value of the non-contact measurement system and the actual value of withers height, body length, chest girth, and chest width were 2.41%, 3.18%, 4.37%, and 6.12%, respectively. The results showed that the proposed method can be used as an automatic and non-contact approach for measuring animal body size.