Bovines Muzzle Classification Based on Machine Learning Techniques

Abstract Bovines muzzle classification is considered as a biometric classifier to maintain the safety of bovines and guarantee the livestock products. This paper presents two different bovines classifications models using Artificial Neural Network (ANN) and K-Nearest Neighbor Classifier (KNN). The proposed ANN model consists of three phases; pre-processing, feature extraction and classifications. Pre-processing techniques; histogram equalization and mathematical morphology filtering has been used. The ANN model use Segmentation-based Fractal Texture Analysis (SFTA) for extract muzzle features. The proposed KNN model consists of two phases; Expectation Maximization image segmentation and classification. Expectation Maximization image segmentation (EM) depends on extracts bovine image color and texture feature extraction. The experimental result evaluation proves the advancement of KNN model than ANN as it achieves 100% classification accuracy in case of increase number of classification groups to twenty-five compared to 92.76% classification accuracy achieved from ANN classification model.