Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize

Abstract

Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping.

Plant phenotyping relevance

3D画像再構成パイプラインを比較・評価し、圃場トウモロコシ根の表現型取得性能を検証する研究であり、フェノタイピング手法が中心です。

Code and data availability

The paper's data availability statement provides two public, paper-specific assets: a GitHub repository with the scripts used to run the 3D reconstruction pipeline comparison, and a Cyverse archive containing all 60 resulting 3D root point cloud models from the twelve field-grown maize genotypes.