3D Sorghum Reconstructions from Depth Images Identify QTL Regulating Shoot Architecture.

Abstract

Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

Plant phenotyping relevance

深度画像から3D植物再構成と形質自動計測を行う半自動パイプラインを開発し、ソルガムの草型形質を取得する方法が研究の中心である。

Code and data availability

The authors explicitly deposit their image acquisition/processing and QTL mapping code (C++, Bash, Python, R) plus genotype/phenotype data on GitHub, and per-plant depth images, RGB images, and segmented meshes on Dryad. Both are paper-specific, public, and actionable.