  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.
