An annotated grain kernel image database for visual quality inspection.

Sci Data

School of Computer Science and Engineering, Faculty of Engineering, The University of New South Wales, Sydney, 2052, Australia.

Published: November 2023

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632488PMC
http://dx.doi.org/10.1038/s41597-023-02660-8DOI Listing

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