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<p>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.</p>

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X-ray computed tomography images of wheat kernels and reconstructed three-dimensional shapes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

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  • Zhixiao Shi,
  • Mingxu Wang,
  • Yingxin Xie,
  • Bingyan Gao,
  • Tongwei Wang

摘要

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.