Cattle Breed Classification Techniques: Framework and Algorithm Evaluation

Abstract

Cattle farmers having cattles from different regions may have different vaccines requirements due to variations in their genetic makeup, environmental conditions, and disease prevalence. This research study focuses on efficiently classifying Cattles into their respective breeds using various machine learning frameworks and algorithms. The study utilized a dataset of images representing different cattle breeds.Pytorch and Tensorflow frameworks, along with algorithms such as Residual Network (ResNet), Convolutional Neural Network (CNN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Random Forest, and K-Nearest Neighbors (KNN) have been used for classificaion of cows. To evaluate the efficiency of these frameworks and algorithms, the study used several machine learning parameters, including Precision-Recall Curve, Learning Curve, Feature Importance, ROC Curve, Accuracy, Precision, Recall, and confusion matrix. The research findings suggest that machine learning techniques can be highly beneficial in accurately classifying cattle breeds, with potential applications in animal breeding, veterinary research, livestock management, and online cattle trading. As the demand for effective livestock supply chain tracking and identification systems increases, this research holds implications for enhancing biosecurity and food safety regulations. Notably, the Pytorch framework demonstrated the best performance among the tested classification models, achieving an impressive accuracy rate of 87.6%.