Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Abstract

In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Considering all this, this research was undertaken to evaluate machine learning (ML) algorithms as potential tools for evaluating 52-year data for sheep. Data was appropriately prepared was done before analysis. Winsorization was done for outlier removal. Principal component regression (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA, PCA+ FS, and FS bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991,0.991,0.991, 0.99, 0.99,0.984,0.984,0.957, 0.949.0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights 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.