Estimation of biometric parameters from cattle rump using free-hand scanning and a 3D data processing algorithm

The quantity and quality of phenotypic data recovered from farm animals became a bottleneck for breeding programmes, and new tools are required to overcome this problem. This study evaluated the use of a portable structured light scanner and a 3D modelling to recover biometric information of the rump region in cattle. Virtual 3D models were created based on coordinates extracted from the points-cloud obtained through reverse engineering. A MATLAB algorithm was implemented to identify reference points, which were used to automatically calculate rump width, length, and angle. Results were compared to measurements performed directly in vivo and in the 3D models. There was no difference among rump parameter values obtained among biometry methods, though an interaction with body condition score was observed for rump width. The algorithm allowed evaluating correlations within biometric parameters, as well as extracting silhouettes of selected areas to evaluate differences caused by the mobilisation of subcutaneous fat.