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

TL;DR

This systematic survey reviews 58 publicly available computer vision datasets for livestock, highlighting their characteristics, applications, and the critical need for more diverse, high-quality annotated data to advance precision livestock farming.

Contribution

First comprehensive review of livestock CV datasets, analyzing their features, applications, and identifying key challenges and opportunities for future dataset development.

Findings

Almost half of datasets focus on cattle

Individual animal detection is the dominant application

Limited high-quality, diverse datasets hinder PLF progress

Abstract

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.