  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.
