Ensemble Learning Algorithm for Cattle Breed Identification using Computer Vision Techniques

This research paper introduces a robust ensemble learning algorithm tailored for cattle breed identification, making use of the integration of cutting-edge computer vision techniques. The methodology combines Convolutional Neural Networks (CNN), YOLO object detection, canny edge detection, k-means image segmentation, and greyscale imaging to overcome inherent limitations in existing methodologies. Addressing challenges associated with obscured features and the presence of dirt on cows, the framework ensures precise and accurate breed prediction. The algorithm adopts a pipeline, encompassing critical stages such as cow identification, optimized resizing, k-means image segmentation, greyscale conversion, and canny edge detection. Through the union of these techniques, the ensemble learner, employing a voting-based mechanism, achieves outstanding performance in the classification of cattle breeds. This research contributes to the advancement of state-of-the-art methodologies for cattle breed identification, providing a foundation for improved decision-making processes in agricultural and livestock management.