Body condition score (BCS) is considered as one of the most important indices for managing dairy cows, which is used to evaluate fat cover and changes in body condition. Dairy farmers should be aware of their cows BCS to be able to identify the patient cows on time and manage diets when needed. In this study, we have introduced a new index which uses Radial Descriptor Lines (RDL) for BC scoring. Based on the fact that the fatter the cow the smoother the back surface, we hypothesised that the changes on the cow’s back at different BCSs could be tracked through the changes on the radial lines centred on the hook bone emitting outward on the surface of the cow’s back.

Images were captured using a Kinect sensor installed in the milking parlour of a dairy farm. To provide the required data for model development and assessment, 165 images were captured from 55 cows with different BCSs. Algorithms were developed in MATLAB environment. The consecutive steps designed in the algorithm were firstly distinguishing the hook bone based on the local maxima on the depth data taken from the Kinect sensor from the cow’s back. Secondly, radial descriptor lines were taken out of the back surface with interval angles of 1 degree outward the hook bone toward the edges of the cow’s back. Overall variations of the descriptor lines respect to a polynomial modelled datum line were measured and used as the extracted features. To include the most related and exclude non-related variations from the BCS estimation model, four orders (from 2 to 5) of polynomial datum curves were tested.

Effective features were selected using correlation-based feature selection (CFS) and fed to artificial neural networks to provide the corresponding BCSs. Results showed a correlation between the estimated BCSs from the model and the scores determined by the experts with a coefficient of determination (R2) of 0.87 and a root mean square error (MSE) of 0.036.