Estimation of Bali Cattle Body Weight Based on Morphological Measurements by Machine Learning Algorithms: Random Forest, Support Vector, K-Neighbors, and Extra Tree Regression

Abstract

In order to forecast and model the weight of cattle a number of techniques have been used. Nonetheless, no machine algorithm has been utilized to estimate the weight of Bali cattle. This article examines the use of machine learning regression to create models for Bali cattle's body weight prediction. The response variables consist of body weight as the dependent variable and  body length, girth circumference, height at wither of 228 male and 211 female cattle of similar ages (285 days). The descriptive statistics of female Bali cattle in our investigation revealed that the morphological measurements were similar to those documented by other researchers. To predict body weight on the basis of different characteristics, machine learning models such as Random Forest, Support Vector, K-Neighbors, and Extra Tree regressions have been used. Additionally, linear regression was utilized to estimate the body weight for comparison with the traditional approach. The assessment standards used included the determination coefficient, the root mean square error, the average absolute error, and the average absolute percentage error as measures of evaluation efficiency. We found that Linear Regression performs the best among all the regressors for female cattle. Similarly for male, it is about the same as extra tree regression. The machine learning algorithm (MLA) was discovered to furnish more precise estimate of the weight of the body cattle, surpassing the conventional algorithm.