A survey of open-access datasets for computer vision in precision poultry farming

Abstract

Computer vision has progressively advanced precision poultry farming. Despite this substantial increase in research activity, computer vision in precision poultry farming still lacks large-scale, open-access datasets with consistent evaluation metrics and baselines, which makes it challenging to reproduce and validate comparisons of different approaches. Since 2019, several image/video datasets have been published and open-accessed to alleviate the issue of dataset scarcity. However, there is no a dedicated survey summarizing the existing progress. To fill this gap, the objective of this research was to provide the first survey and analysis of the open-access image/video dataset for precision poultry farming. A total of 20 qualified images/video datasets were summarized, including 4 for behavior monitoring, 6 for health status identification, 3 for live performance prediction, 4 for product quality inspection, and 3 for animal trait recognition. Critical points of creating a new image/video dataset, consisting of data acquisition, augmentation, annotation, sharing, and benchmarking, were discussed. The survey provides options for selecting appropriate datasets for model development and optimization while delivering insights into building new datasets for precision poultry farming.