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 raise carbon sequestration from the atmosphere.
However, high-throughput phenotyping to quantify root traits for deeper
field-grown roots remain a challenge. Recently developed open-source
methods use image-based 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 it is not known how the performance of these methods
compares to each other when applied to field-grown roots. We investigate
commonly used open-source pipelines on a test panel of twelve
contrasting maize genotypes grown in real field conditions in this
comparison study [2-6]. We compare 3D point clouds in terms of
number of points, computation time, and model surface density. This
comparison study will provide 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.