Review Details AI for Advanced Cattle Identification and Detection.

The livestock management sector urgently needs effective cattle identification technologies to ensure biosecurity, food safety, and supply chain efficiency. A new systematic review comprehensively analyzes recent advancements in using machine learning and deep learning for this purpose. The review assesses both traditional and modern identification techniques by examining studies from major academic databases. The findings indicate that while classical machine learning methods like K-Nearest Neighbors and Support Vector Machines show good results, deep learning techniques such as Convolutional Neural Networks (CNNs), Residual Networks, and You Only Look Once (YOLO) demonstrate superior performance in complex cognition, detection, and identification tasks. Common feature extraction methods include Local Binary Pattern (LBP), Speeded-Up Robust Features (SURF), and Scale-Invariant Feature Transform (SIFT, with muzzle prints and coat patterns being frequently used features. The review also identifies significant hurdles in cattle identification, including the scarcity of publicly available datasets, challenges with data quality due to environmental variations and animal movement, and the high requirement for real-time processing capabilities. The paper aims to provide valuable insights for researchers, policymakers, and stakeholders to develop scalable, humane, and sustainable livestock management systems.