Image Based Bovine Breed Recognition System

Abstract

Accurate identification of cattle and buffalo breeds is vital for livestock management, genetic conservation, productivity enhancement, and the implementation of national agricultural initiatives. Traditional identification techniques such as ear tagging, branding, and RFID are often invasive, error-prone, and inefficient in large-scale farm environments. Recent advancements in Artificial Intelligence (AI) and Computer Vision (CV) have enabled non-invasive, automated, and highly accurate livestock identification using image-based techniques. This paper presents a comprehensive review of image-based bovine breed recognition systems, analysing research published between 2018 and 2025. It examines the evolution from classical machine learning approaches to advanced deep learning models, including Convolutional Neural Networks (CNNs), transfer learning, attention mechanisms, object detection frameworks (YOLO), and video-based recognition systems. The review also highlights biometric identification methods such as muzzle pattern and facial recognition, lightweight architectures for edge deployment, and the integration of AI models with IoT-enabled smart farm management systems. Finally, key challenges related to dataset limitations, environmental variability, computational constraints, and adoption barriers are discussed, along with future research directions aimed at developing scalable, robust, and real-time AI-driven livestock identification solutions.