A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision

Computer vision applications in livestock are appealing since they enable measurement of traits of interest without the need to directly interact with the animals. This allows the possibility of multiple measurements of traits of interest with minimal animal stress. In the current study, an automated computer vision system was devised and evaluated for extraction of features of interest, as body measurements and shape descriptors, and prediction of body weight in pigs. From the 655 pigs that had data collected 580 had more than 5 frames recorded and were used for development of the predictive models. The cross-validation for the models developed with data from nursery and finishing pigs presented an R-2 ranging from 0.86 (random selected image) to 0.94 (median of images truncated on the third quartile), whereas with the dataset without nursery pigs, the R-2 estimates ranged from 0.70 (random selected image) to 0.84 (median of images truncated on the third quartile). However, overall the mean absolute error was lower for the models fitted without data on nursery animals. From the body measures extracted from the image, body volume, area, and length were the most informative for prediction of body weight. The inclusion of the remaining body measurements (width and heights) or shape descriptors to the model promoted significant improvement of the predictions, whereas the further inclusion of sex and line effects were not significant.