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.

A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops

“…Sorghum plant architecture parameters related to shoot height and leaf area were characterized using Microsoft Kinect cameras and 3D reconstruction of single potted plants. This phenotyping method was applied successfully to identify quantitative trait loci (QTLs) that colocalized with previously reported genomic regions controlling these traits (McCormick et al, 2016).…”

High Fidelity Detection of Crop Biomass QTL From Low-Cost Imaging in the Field

“…Notably, there was minimal overlap between QTL for PAI and those for traits associated with plant bushiness and biomass partitioning rather than biomass production. So, while hemispherical imaging is a powerful tool for assessing the genetic architecture of productivity traits in the field, other more complex imaging techniques or laboratory based methods will be needed to quickly phenotype plant architectural traits 30 .…”

Voxel Carving Based 3D Reconstruction of Sorghum Identifies Genetic Determinants of Radiation Interception Efficiency

“…However, plants are complex 3D structures and data collected from a single or several 2D images can miss or inaccurately estimate for important plant features (McCormick et al, 2016;Thapa et al, 2018). Various approaches attempt to faithfully extract either the full 3D structure of plants or at least some important traits from captured data.…”

“…LIDAR-based reconstruction of maize and sorghum plants using a fixed LIDAR sensor and plants were placed on a rotating platform achieved 0.92 ≤ R 2 ≤ 0.94 for maize and sorghum (Thapa et al, 2018). A time-of-flight cameras (e.g., Microsoft Kinect) were employed to generate 3D models of individual plants from a sorghum RIL population, achieving R 2 = 0.85 with destructively measured leaf area (McCormick et al, 2016). However, these approaches require dedicated equipment unlikely to be available in many plant research labs and LIDAR are not suitable for data with high frequency information and high inter reflections that are typical for vegetation.…”