Sci Data. 2026 Apr 9. doi: 10.1038/s41597-026-07207-1. Online ahead of print.
ABSTRACT
Grain kernel morphology strongly influences aerodynamic loads, breakage susceptibility, and processing performance. Conventional two-dimensional projection imaging and manual measurements lack the accuracy and completeness needed to represent the true three-dimensional structure of kernels. We present an open X-ray computed tomography dataset of wheat kernels that includes axial slice stacks and three-dimensional shape models for 100 individual kernels, together with per-sample morphometrics including principal-axis lengths, elongation index, flatness index, convexity, and sphericity. The dataset supports statistics of kernel shape, enables analysis of internal microstructure from the provided slice stacks, and facilitates high-fidelity discrete element modelling with realistic particle geometry.
PMID:41957421 | DOI:10.1038/s41597-026-07207-1
