Evaluation of Artificial Intelligence Algorithms for the Prediction of Genetic Merit

Abstract

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis. Breeding values were estimated using Best Linear Unbiased Prediction. 13 ML algorithms were evaluated for their ability to predict the breeding values. The variance inflation factor for all features selected through PCA was 1. The correlation coefficients between true and predicted values for Artificial neural networks, Bayesian ridge regression, Classification and regression trees, Genetic Algorithms, Gradient boosting algorithm, K nearest neighbours, MARS algorithm, Polynomial regression, Principal component regression, Random forests, Support Vector Machines, XGBoost algorithm were 0.852, 0.742, 0.869, 0.762, 0.915, 0.781, 0.746, 0.742, 0.746, 0.917, 0.777, 0.915 respectively for breeding value prediction. Random forests had the highest correlation coefficients. A total of 13 machine learning models were developed from the prediction of breeding values in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies for breeding value prediction.

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

“…A tenfold cross validation approach was reported to train the best model by Huma and Iqbal, (2019) which also correlated with our results. Hamadani et al (2022) also used principal component regression for the prediction of genetic merit in sheep and reported a correlation coefficient to 0.74 between true and predicted labels.…”