An annotated grain kernel image database for visual quality inspection
This dataset addresses a domain-specific problem for researchers and practitioners in agriculture and machine vision by enabling improved grain quality inspection, though it is incremental as it builds on existing data collection methods.
The authors tackled the lack of a comprehensive dataset for visual quality inspection of grain kernels by creating GrainSet, a database with over 350K annotated images of four cereal types from multiple regions, and provided benchmark classification results using a deep learning model.
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.