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

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.

Plant phenotyping relevance

ソルガムのRGB画像から3D植物体を自動再構成し、光 interception 特性を推定する高スループット手法の開発が中心であるため。

Code and data availability

The paper's voxel carving analysis code is explicitly stated as publicly available on GitHub. The raw images, 3D reconstructions, and trait values were only promised for future DataDryad deposit with no URL, so they are not actionable. The FigShare deposit contains genetic marker data (molecular omics), not phenotyping