Abstract
Computer vision systems (CVS) have many applications in livestock, for example, they allow measuring traits of interest without the need for directly handling the animals, avoiding unnecessary animal stress. The objective in the current study was to devise an automated CVS for extraction of variables as body measurements and shape descriptors in pigs using depth images. These features were then tested as potential predictors of live body weight (BW) using a 5-fold cross validation (CV) with different modeling approaches: traditional multiple linear regression (LR), partial least squares (PLS), elastic networks (EL), and artificial neural networks (ANN). The devised CVS could analyze and extract features from a video fed at a rate of 12 frames per second. This resulted in a dataset with more than 32 thousand frames from 655 pigs. However, only the 580 pigs with more than 5 frames recorded were used for the development of the predictive models. From the body measures extracted from the images, body volume, area and length presented the highest correlations with BW, while widths and heights were highly correlated with each other (Figure 1). The results of the CV of the models developed for predictions of BW using a selected set of the more significant variables presented mean absolute errors (MAE) of 3.92, 3.78, 3.72, and 2.57 for PLS, LR, EN, and ANN respectively (Table 1). In conclusion, the CVS developed can automatically extract relevant variables from 3D images and a fully connected ANN with 6 hidden layers presented the overall best predictive results for BW.
3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries