Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

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 compare two AI techniques for predicting 12-month body weights animals; viz. Principal Component regression (PCR) Ordinary Least Squares (OLS) datasets Corriedale sheep spanning 11 years. Methods: PCR models were trained by varying proportions training testing datasets. The dataset subject before analysis tested (PCA dataset). A separate also created feature selection PCA (PCA+FS dataset) variables. Result: highest correlation coefficients between test predicted variables PCA+FS among multiple using 0.982 0.9741. terms error, R2 or coefficient, performed better than dataset. second principal component had explained variance in OLS (86.16%) coefficient determination (R2) obtained 0.980. It concluded that both algorithms study satisfactorily their prediction with performing value.