Overview of data collection and annotations for GrainSet.
Examples and distributions of sample information in GrainSet.
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels. The database contains more than 350K single-kernel images with experts’ annotations. The grain kernels used in the study consist of four types of cereal grains including wheat, maize, sorghum and rice, and were collected from over 20 regions in 5 countries. The surface information of each kernel is captured by our custom-built device equipped with high-resolution optic sensor units, and corresponding sampling information and annotations include collection location and time, morphology, physical size, weight, and Damage & Unsound grain categories provided by senior inspectors. In addition, we employed a commonly used deep learning model to provide classification results as a benchmark. We believe that our GrainSet will facilitate future research in fields such as assisting inspectors in grain quality inspections, providing guidance for grain storage and trade, and contributing to applications of smart agriculture.
The GrainSet dataset provides a comprehensive collection of high-quality images for cereal grain inspection. It includes over 350K images captured from four types of grains: Wheat, Maize, Sorghum and Rice. The dataset supports tasks such as fine-grained recognition and detection.
The GrainSet dataset is licensed under the Creative Commons BY-NC-SA 4.0 license. Note that All data must not be used for commercial purposes.
You can access the dataset using the figshare links below:
@article{fan2023annotated,
title={An annotated grain kernel image database for visual quality inspection},
author={Fan, Lei and Ding, Yiwen and Fan, Dongdong and Wu, Yong and Chu, Hongxia and Pagnucco, Maurice and Song, Yang},
journal={Scientific Data},
volume={10},
number={1},
pages={778},
year={2023},
publisher={Nature Publishing Group UK London}
}