We present a machine vision-based database named GrainSet that has the potential to revolutionize visual quality inspection of grain kernels. With over 350K single-kernel images and experts’ annotations, this database provides a valuable resource for researchers and professionals in the field.
The database encompasses four types of cereal grains – wheat, maize, sorghum, and rice – collected from more than 20 regions in 5 countries. This comprehensive dataset ensures a diverse range of samples, capturing the variations in grain quality across different geographical locations and growing conditions.
One of the key strengths of GrainSet lies in the surface information captured for each kernel using a custom-built device equipped with high-resolution optic sensor units. This level of detail enables inspectors to analyze the morphology, physical size, weight, and other important characteristics of the grain kernels. Additionally, the database includes relevant sampling information and annotations such as collection location and time, as well as Damage & Unsound grain categories provided by senior inspectors.
To further enhance the capabilities of GrainSet, a deep learning model has been employed to provide classification results as a benchmark. This allows researchers to compare their own algorithms or techniques against a well-established baseline. The integration of deep learning in grain quality inspection opens up possibilities for automation and streamlining of inspection processes.
The potential applications of GrainSet are broad and impactful. By assisting inspectors in grain quality inspections, this database can significantly improve the efficiency and accuracy of the assessment process. Additionally, it can provide valuable guidance for grain storage and trade, helping stakeholders make informed decisions based on the quality of the grains. Moreover, GrainSet can contribute to the development of smart agriculture solutions by leveraging machine vision for real-time quality assessment of grain crops.
In the future, we can expect to see further advancements in machine vision technology for grain quality inspection. With the continuous development of deep learning algorithms and the availability of large-scale annotated databases like GrainSet, we are likely to witness more accurate and automated inspection systems. Such systems could have a profound impact on the grain industry, ensuring the quality and safety of grains throughout the supply chain.