An extensive image dataset for deep learning-based classification of rice kernel varieties in Bangladesh

Abstract

This article introduces a comprehensive dataset developed in collaboration with the Bangladesh Institute of Nuclear Agriculture (BINA) and the Bangladesh Rice Research Institute (BRRI), featuring high-resolution images of 38 local rice varieties. Captured using advanced microscopic cameras, the dataset comprises 19,000 original images, enhanced through data augmentation techniques to include an additional 57,000 images, totaling 76,000 images. These techniques, which include transformations such as scaling, rotation, and lighting adjustments, enrich the dataset by simulating various environmental conditions, providing a broader perspective on each variety. The diverse array of rice strains such as BD33, BD30, BD39, among others, are meticulously detailed through their unique characteristics—color, size, and utility in agriculture—providing a rich resource for research. This augmented dataset not only enhances the understanding of rice diversity but also supports the development of innovative agricultural practices and breeding programs, offering a critical tool for researchers aiming to analyze and leverage rice genetic diversity effectively.