Spatially Resolved Wheat Kernel Vitreousness Quantification via Hyperspectral Imaging

Seok Won Jeong's dataset presents a digital phenotyping framework for wheat kernels. It uses hyperspectral imaging and spectral unmixing to quantify kernel hardness, vitreousness, and creaseness at both kernel and cultivar levels. The dataset was last updated on 2026-05-18.

Use Cases

• Quantifying vitreousness as a continuous spatial index based on pixel-level spectral unmixing.
• Analyzing associations between vitreousness and kernel protein content or protein-to-starch ratio.
• Investigating the independence of grain quality dimensions like hardness, vitreousness, and creaseness.
• Developing scalable, non-destructive phenotyping methods for intra-kernel heterogeneity.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• Framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Dataset size is 16.7 KB, indicating a very limited scope.

Provenance

Source

figshare

Collection Method

Hyperspectral imaging and spectral unmixing applied to a diverse panel of common wheat.

Freshness

Last updated 2026-05-18 04:17:17; freshness should be verified.