This article presents a multimodal dataset of Canadian wild rice ( Zizania palustris ) kernels, combining high-resolution RGB imagery and visible–near-infrared (VNIR) hyperspectral reflectance data for post-harvest quality characterization. The dataset comprises paired RGB images and VNIR hyperspectral scans of individual wild rice kernels representing eight quality categories: Healthy–Large, Healthy–Medium, Discoloured–Low, Discoloured–High, Broken–Low, Broken–High, Insect-damaged, and Unhulled. RGB images were acquired under controlled laboratory conditions and processed into standardized 512 × 512 pixel single-kernel patches, with additional augmented variants generated using controlled geometric and photometric transformations. VNIR hyperspectral data were acquired in the 400–1000 nm wavelength range using a hyperspectral imaging system, followed by radiometric correction, automated kernel segmentation, and per-kernel spectral extraction. The dataset includes raw hyperspectral files, dark and white reference frames, reflectance-corrected hypercubes, segmentation masks, per-kernel mean spectral profiles, paired RGB images, and associated metadata. This resource is intended to support research in automated grain grading, defect detection, and multimodal machine-learning applications in spectroscopy, computer vision, and post-harvest quality assessment.
Sikiru et al. (Sun,) studied this question.