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Application of X-ray computed tomography to analyze the structure of sorghum grain.

Plant methods · 11 Jan 2022 · 10.1186/s13007-022-00837-7

Abstract

Background The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality. Results Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics. Conclusions Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.

Plant phenotyping relevance

X線CTを用いてソルガム穀粒の三次元形質を定量化するフェノタイピングパイプラインを開発・検証しており、形質取得手法が研究の中心である。

abstractPhenotypic measurements were extracted from CT scans of grain sorghum caryopses.
abstractCT derived estimates were strongly correlated with ground truth measurements
abstractPresented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses

Code and data availability

The article describes a CT-based sorghum grain phenotyping pipeline, but no public phenotype dataset, CT image data, analysis code, or trained classifier deposit is mentioned. The only supplement is a figure (Additional file 1: Fig. S1) illustrating the data extraction workflow, not data or code. No availability or URL

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