Classification of Body Weight in Beef Cattle via Machine Learning Methods: A Review
Moad Hakem, Zakaria Boulouard, Mohamed Kissi
Abstract
Livestock producer’s profits are generally linked to the weight of their animals. This will allow them to better plan their supply of the worldwide increasing demand on meat An interesting approach to address the issue of final performance prediction is to use machine learning techniques. In this paper, we will review a number of papers that have attempted to address this issue using machine learning algorithms such as multiple regressions, partial least squares regression, random forests, naive Bayes, support vector machines and artificial neural networks. Based on previously collected datasets of animals at different stages of growth, these algorithms attempt to predict the final performance of new animals. The quality of the predictions is measured using different parameters such as accuracy, sensitivity, mean absolute error percentage and root mean square error.