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\begin{document}

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



\author[1,1]{suxing liu}%
\author[1,1]{Wesley Paul Bonelli}%
\author[1,1]{Peter Pietrzyk}%
\author[1,1]{Alexander Bucksch}%
\affil[1]{University of Georgia}%


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  \date{May 25, 2024}


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\begin{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.%
\end{abstract}%



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