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

Crozier D, Riera-Lizarazu O, Rooney WL

Published: 13 January 2022 in Plant methods
Keywords: Grain morphology, Grain quality, Machine learning, Phenotyping, Random forest, Segmentation
Pubmed ID: 35016682
DOI: 10.1186/s13007-022-00837-7

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.