Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency

Authors

Mathieu Gaillard, Purdue UniversityFollow
Chenyong Miao, University of Nebraska - Lincoln
James Schnable, University of Nebraska-LincolnFollow
Bedrich Benes, Purdue UniversityFollow

ORCID IDs

Mathieu Gaillard https://orcid.org/0000-0003-0840-5654

Chenyong Miao https://orcid.org/0000-0002-0904-3707

James C. Schnable https://orcid.org/0000-0001-6739-5527

Bedrich Benes https://orcid.org/0000-0002-5293-2112

Date of this Version

2020

Citation

Gaillard M, Miao C, Schnable JC, Benes B. Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency. Plant Direct. 2020;00:1–16. https://doi. org/10.1002/pld3.255

Comments

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License. © 2020 The Authors.

Abstract

Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions’ accuracy.