Automatic body condition scoring system for dairy cows based on depth-image analysis

Body condition score (BCS) is an important management tool in the modern dairy industry, and one of the basic techniques for animal welfare and precision dairy farming. The objective of this study was to use a vision system to evaluate the fat cover on the back of cows and to automatically determine BCS. A 3D camera was used to capture the depth images of the back of cows twice a day as each cow passed beneath the camera. Through background subtraction, the back area of the cow was extracted from the depth image. The thurl, sacral ligament, hook bone, and pin bone were located via depth image analysis and evaluated by calculating their visibility and curvature, and those four anatomical features were used to measure fatness. A dataset containing 4820 depth images of cows with 7 BCS levels was built, among which 952 images were used as training data. Taking four anatomical features as input and BCS as output, decision tree learning, linear regression, and BP network were calibrated on the training dataset and tested on the entire dataset. On average, the BP network model scored each cow within 0.25 BCS points compared to their manual scores during the study period. The measured values of visibility and curvature used in this study have strong correlations with BCS and can be used to automatically assess BCS with high accuracy. This study demonstrates that the automatic body condition scoring system has the possibility of being more accurate than human scoring.