An annotated grain kernel image database for visual quality inspection.

Abstract

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.

Plant phenotyping relevance

穀粒画像と形態・サイズ・重量・損傷状態の注釈を大規模に整備した再利用可能なデータセットで、カスタム撮像装置とベンチマーク分類も含むため、植物器官の表現型取得・解析が中心です。

Code and data availability

The paper's GrainSet database (annotated single-kernel grain images with DU-grain, weight, size, and mask annotations) is publicly deposited on Figshare under CC BY 4.0, split into four species sub-datasets plus tiny/raw previews, and the authors' validation code and trained models are released on GitHub.