Cattle individuality recognition has emerged as a critical aspect of contemporary precision livestock farming. Biometric identifiers, specifically muzzle and facial features, are gaining traction as key components in this domain. This paper proposes a novel multi-biometric approach for enhanced cattle individuality recognition in precision livestock farming. The system leverages advanced object detection models, specifically YOLOv8, to identify cattle based on muzzle and facial features. Pre-processing techniques and data augmentation strategies are employed to improve model robustness. The proposed method is implemented as a real-time edge device application, demonstrating its potential for practical agricultural use. A meticulously curated dataset ( https://github.com/RahulRaman2/Indian-Cattle-Biometric-Database ) exceeding 5,000 cattle face and muzzle images is utilized for model training, achieving an accuracy of 90.39%. Further improvements in accuracy can be achieved through continued refinement of the training dataset, optimization of the model parameters, and exploration of ensemble learning techniques.