An intelligent system for taurine breed recognition: preliminary results

Abstract

Uncontrolled crossbreeding between zebus and taurine cattle is jeopardizing the genetic heritage of West African taurines and their specific ability to resist trypanosomosis. to achieve any successful conservation policy for this species, it is crucial to accurately identify purebred taurines. Techniques in use today include empirical method and biological analysis. We offer in this paper a supervised Machine Learning approach of pure-bred taurine recognition. Five algorithms were trained using morphological data from hundreds of cows. Each of the models produced promising results. The RBF non linear SVM performs the best with up to 87% accuracy and 0.9308 of AUC. Furthermore, the correlation coefficients allowed to define the most discriminating morphological trait.