A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions

TL;DR

This systematic review analyzes machine learning and deep learning techniques for cattle identification, highlighting datasets, methods, and future research directions in vision-based livestock management.

Contribution

It provides a comprehensive analysis of ML and DL models used for cattle identification, including feature extraction methods and key distinguishing features like muzzle prints and coat patterns.

Findings

Support vector machine (SVM), KNN, and ANN are common ML models.

CNN, ResNet, YOLO, and Faster R-CNN are popular DL models.

Features like muzzle prints and coat patterns are most used for identification.

Abstract

Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been applied in precision livestock management, including critical disease detection, vaccination, production management, tracking, and health monitoring. This paper offers a systematic literature review (SLR) of vision-based cattle identification. More specifically, this SLR is to identify and analyse the research related to cattle identification using Machine Learning (ML) and Deep Learning (DL). For the two main applications of cattle detection and cattle identification, all the ML based papers only solve cattle identification problems. However, both detection and identification problems were studied in the DL based papers. Based on our survey report, the most used ML models for cattle identification were support vector machine (SVM), k-nearest neighbour (KNN), and artificial neural network (ANN). Convolutional neural network (CNN), residual network (ResNet), Inception, You Only Look Once (YOLO), and Faster R-CNN were popular DL models in the selected papers. Among these papers, the most distinguishing features were the muzzle prints and coat patterns of cattle. Local binary pattern (LBP), speeded up robust features (SURF), scale-invariant feature transform (SIFT), and Inception or CNN were identified as the most used feature extraction methods.