Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review

The paper presents a comprehensive review of machine learning and deep learning techniques for cattle identification, emphasizing the effectiveness of traditional methods like K-Nearest Neighbors and Support Vector Machines, alongside advanced approaches such as Convolutional Neural Networks, Residual Networks, and You Only Look Once. Key feature extraction methods discussed include Local Binary Pattern, Speeded-Up Robust Features, and Scale-Invariant Feature Transform, with a focus on identifying muzzle prints and coat patterns. This review is significant for practitioners as it addresses challenges such as limited datasets, data quality issues, and the need for real-time processing, providing insights for developing scalable and effective cattle identification systems in livestock management.