Summary of Public Computer Vision Datasets For Precision Livestock Farming: a Systematic Survey, by Anil Bhujel et al.
Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey
by Anil Bhujel, Yibin Wang, Yuzhen Lu, Daniel Morris, Mukesh Dangol
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the first systematic survey of publicly available computer vision (CV) datasets for precision livestock farming (PLF). The study analyzed 58 public datasets covering different species of livestock, with cattle being the most represented. The datasets were found to be dominated by individual animal detection and color imaging applications. The characteristics and baseline uses of the datasets are discussed, highlighting implications for animal welfare advocates. Challenges and opportunities are also explored to inspire further efforts in developing high-quality annotated datasets from diverse environments, animals, and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers look at computer vision (CV) datasets used for precision livestock farming (PLF). PLF helps farmers take better care of their animals by monitoring their health and growth. CV uses cameras and computers to help collect data about the animals. The goal is to make it easier to develop new systems that use AI to analyze this data. Right now, collecting and organizing this data can be time-consuming and costly. This study finds 58 public datasets that can be used for PLF research. Most of these datasets are for cattle, with some for pigs, chickens, and other animals too. |
Keywords
» Artificial intelligence » Precision
