Deep Learning for Cattle Face Identification

Cattle farming plays a crucial role in meeting the increasing nutritional demands of growing populations. Therefore, it is important to have efficient methods of identifying individual cattle for effective livestock management, maintaining the herd’s health, and ensuring farms’ financial sustainability. Traditional identification methods, such as ear tags and branding, are not ideal due to theft issues and discomfort for the animals, while microchips, although accurate, present logistical and ethical challenges. Therefore, novel identification methods are required. This work aims to review all current innovative cattle identification systems, specifically utilizing facial recognition technology based on deep learning. The conducted research identifies and summarizes state-of-the-art approaches for data preprocessing, feature extraction, model training, and testing. Furthermore, a comparative study on the performance metrics of the identified works takes place. Challenges associated with lighting conditions, dataset quality, processing speed, and practical implementation in dynamic farm environments, such as the motion of cattle, are also reported, indicating the need for a unified framework to confront implementation issues.