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

arXiv: https://arxiv.org/abs/2210.09215 · HTML: https://mlsanity.com/paper/2210.09215/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 114 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning](https://mlsanity.com/paper/2006.09205/) (2020)
- [Multi-views Embedding for Cattle Re-identification](https://mlsanity.com/paper/1902.04886/) (2019)
- [Validation of Biometric Identification of Dairy Cows based on Udder NIR Images](https://mlsanity.com/paper/1811.09918/) (2018)

## Built on by (most cited first, 9 of 9)

- [Zero-Shot Stance Detection using Contextual Data Generation with LLMs](https://mlsanity.com/paper/2405.11637/) (2024)
- [Livestock feeding behaviour: A review on automated systems for ruminant monitoring](https://mlsanity.com/paper/2312.09259/) (2023)
- [Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey](https://mlsanity.com/paper/2406.10628/) (2024)
- [When Language Model Guides Vision: Grounding DINO for Cattle Muzzle Detection](https://mlsanity.com/paper/2509.06427/) (2025)
- [CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2509.11219/) (2025)
- [The object detection model uses combined extraction with KNN and RF classification](https://mlsanity.com/paper/2405.05551/) (2024)
- [Cross-Camera Cow Identification via Disentangled Representation Learning](https://mlsanity.com/paper/2602.07566/) (2026)
- [Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review](https://mlsanity.com/paper/2606.15655/) (2026)
- [ReCowGnition: A Realistic Biometric Benchmark for Cow Face Recognition](https://mlsanity.com/paper/2607.22071/) (2026)

## Abstract (arXiv)

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.