Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

Abstract

Background: Artificial intelligence (AI) is transforming all spheres of life and it has the potential to revolutionize animal husbandry as well. In this regard, an attempt was made to compare two AI techniques for predicting 12-month body weights of animals; viz. Principal Component regression (PCR) and Ordinary Least Squares (OLS) for datasets of Corriedale sheep spanning 11 years. Methods: PCR models were trained by varying proportions of training and testing datasets. The dataset was subject to PCR before analysis and tested (PCA dataset). A separate dataset was also created by feature selection of the PCA (PCA+FS dataset) variables. Result: The highest correlation coefficients between test and predicted variables for two datasets (PCA dataset and PCA+FS dataset) created among the multiple models trained using PCR were 0.982 and 0.9741. In terms of error, R2 or correlation coefficient, the PCA dataset performed better than the PCA+FS dataset. The second principal component had the highest explained variance in OLS (86.16%) and the highest coefficient of determination (R2) using OLS was obtained for the PCA dataset viz. 0.980. It is concluded that both the algorithms tested in this study were satisfactorily trained in their prediction of the body weights with OLS performing better than PCA in terms of R2 value.