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

Md Ekramul Hossain, Ashad Kabir, Lihong Zheng, Dave Swain, Shawn McGrath, Jonathan Medway

Research output: Contribution to journalArticlepeer-review


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). This study retrieved 731 studies from four online scholarly databases. Fifty-five articles were subsequently selected and investigated in depth. For the two main applications of cattle detection and cattle identification, all the ML based papers only solved 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 neighbor (KNN), and artificial neural network (ANN). Convolution 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 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. This paper details important factors to consider when choosing a technique or method. We also identified major challenges of cattle identification. There are few publicly available datasets and dataset quality is affected by the wild environment and movement while collecting data. The processing time is a critical factor for a real-time cattle identification system. Finally, a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.
Original languageEnglish
Pages (from-to)138-155
JournalArtificial Intelligence in Agriculture
Early online date2022
Publication statusPublished - 2022

Grant Number

  • 103328


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