# Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey

arXiv: https://arxiv.org/abs/2406.10628 · HTML: https://mlsanity.com/paper/2406.10628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 106 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Pretraining boosts out-of-domain robustness for pose estimation](https://mlsanity.com/paper/1909.11229/) (2019)
- [Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning](https://mlsanity.com/paper/2006.09205/) (2020)
- [Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset](https://mlsanity.com/paper/2105.01938/) (2021)
- [A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions](https://mlsanity.com/paper/2210.09215/) (2022)
- [Voxelized 3D Feature Aggregation for Multiview Detection](https://mlsanity.com/paper/2112.03471/) (2021)
- [CattleEyeView: A Multi-task Top-down View Cattle Dataset for Smarter Precision Livestock Farming](https://mlsanity.com/paper/2312.08764/) (2023)
- [Livestock Monitoring with Transformer](https://mlsanity.com/paper/2111.00801/) (2021)
- [Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras](https://mlsanity.com/paper/2111.10971/) (2021)
- [Vision-based Behavioral Recognition of Novelty Preference in Pigs](https://mlsanity.com/paper/2106.12181/) (2021)
- [High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning](https://mlsanity.com/paper/2308.03861/) (2023)

## Built on by (most cited first, 1 of 1)

- [A survey of datasets for computer vision in agriculture](https://mlsanity.com/paper/2502.16950/) (2025)

## Abstract (arXiv)

Technology-driven precision livestock farming (PLF) empowers practitioners to monitor and analyze animal growth and health conditions for improved productivity and welfare. Computer vision (CV) is indispensable in PLF by using cameras and computer algorithms to supplement or supersede manual efforts for livestock data acquisition. Data availability is crucial for developing innovative monitoring and analysis systems through artificial intelligence-based techniques. However, data curation processes are tedious, time-consuming, and resource intensive. This study presents the first systematic survey of publicly available livestock CV datasets (https://github.com/Anil-Bhujel/Public-Computer-Vision-Dataset-A-Systematic-Survey). Among 58 public datasets identified and analyzed, encompassing different species of livestock, almost half of them are for cattle, followed by swine, poultry, and other animals. Individual animal detection and color imaging are the dominant application and imaging modality for livestock. The characteristics and baseline applications of the datasets are discussed, emphasizing the implications for animal welfare advocates. Challenges and opportunities are also discussed to inspire further efforts in developing livestock CV datasets. This study highlights that the limited quantity of high-quality annotated datasets collected from diverse environments, animals, and applications, the absence of contextual metadata, are a real bottleneck in PLF.