Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements.

The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 ± 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used. The age of the pigs (180 ± 5 days) was also included as a nominal predictor. In total, 218 individual measurements were obtained after data preprocessing. In the Sujiang pig dataset, BW had a significantly positive and high linear relationship with BH, BL, CW, HW, and CC resulting in values of 0.66, 0.72, 0.81, 0.84, and 0.88, respectively (p < 0.01). Artificial neural network (ANN), K-nearest neighbors (KNN), and classification and regression tree (CART) algorithms were used to predict BW. Overall, the ANN algorithm outperformed the other algorithms in this pig dataset according to the goodness of fit criteria of R