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.

我们提出了一个名为GrainSet的基于机器视觉的数据库，用于谷物颗粒的视觉质量检测，其中包含超过350K个带有专家注释的单颗粒图像。我们还采用了常用的深度学习模型作为基准提供分类结果，相信我们的GrainSet将促进谷物质量检测的辅助、谷物存储和贸易的指导以及智能农业应用的发展。

一份用于视觉品质检查的注释谷粒图像数据库