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Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011) Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

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• TITLE: Toward an Intelligent System for Taurine Cattle Recognition

  AUTHORS: Fulbert Bembamba, Frédéric T. Ouédraogo, Soudré Albert, Amadou Traoré

  KEYWORDS: Machine Learning, Trypanosomosis, Purebred Taurine, Accuracy, Model

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.14 No.1, February 28, 2022

  ABSTRACT: Unlike zebus, taurine cattle have the natural ability to resist trypanosomosis, a parasitic disease endemic to the humid areas of West Africa. However, repeated crossbreeding between zebus and taurine cattle is jeopardizing the genetic heritage of the Taurines and their ability to resist trypanosomosis. To strengthen protection and conservation efforts, it is essential to accurately distinguish purebred taurines from crossbreds. In this study, five Machine Learning models were built using morphological data collected from 1968 cattle. These models were trained to determine whether a given individual is purebred taurine or not. The classifiers yielded promising results. The random forest model and RBF Kernel SVM performed the best with up to 86% and 85% accuracy respectively. Moreover, the study of the correlation coefficients and the feature importance scores allowed us to define the most discriminating morphological traits.