In the current context where biosecurity, food safety, and supply chain efficiency are increasingly critical, the demand for effective cattle identification technology has become more urgent. This paper systematically reviews recent research on cattle identification using machine learning and deep learning techniques. By conducting full-text reviews of literature from major academic databases, it summarizes the effectiveness of traditional and modern cattle identification methods.
Classical machine learning techniques, such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), have shown good results in cattle identification; however, deep learning techniques, like Convolutional Neural Networks (CNN), Residual Networks (ResNet), and You Only Look Once (YOLO), perform better in cognition, detection, and identification tasks.
Feature extraction relies on common techniques like Local Binary Pattern (LBP), Speeded-Up Robust Features (SURF), and Scale-Invariant Feature Transform (SIFT), with key features commonly used in these studies including muzzle prints and coat patterns. The review also highlights major hurdles in cattle identification, such as the limited number of publicly accessible datasets, issues with data quality affected by environmental changes and animal mobility, and the high demand for real-time processing capabilities.
Ultimately, the paper aims to inform researchers, policymakers, and stakeholders about implementing scalable, humane, and effective cattle identification systems to achieve sustainable livestock management.
Blogger's Review: This paper offers an insightful exploration of cutting-edge technologies in cattle identification, particularly highlighting the significant advantages of deep learning in enhancing recognition accuracy. The proposed solutions addressing the current dataset limitations and environmental impacts are noteworthy, and future research should continue to focus on algorithm optimization and data quality improvement.