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\begin{document}

\title{3D phenotyping of peach tree canopy architecture using terrestrial laser
scanning}



\author[1]{Jordan Knapp-Wilson}%
\author[2]{Rafael Bohn Reckziegel}%
\author[1]{Alexander Bucksch}%
\author[1]{Dario J Chavez}%
\affil[1]{University of Georgia}%
\affil[2]{University of Freiburg}%


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  \date{May 20, 2024}


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\begin{abstract}
Throughout history, pomologists have developed various trainings systems
for temperate fruit trees to improve light interception, fruit yield,
and fruit quality. To achieve this, these training systems enforce
certain branch and canopy morphologies upon the tree. Quantifying
architecture could aid the selection for trees that require less pruning
or naturally excel in specific growing/training system conditions. Tree
architecture is also directly associated with resource optimization,
funneling what nutrients the plant absorbs into the most efficient,
high-yielding configuration possible. In peaches {[}Prunus persica (L.)
Batsch{]}, branching indices (BIs) have been developed in attempts to
quantify tree architecture. BIs can effectively focus on a particular
area of tree architecture (e.g., an index focused on branching density,
or BDi), producing quantitative measurements that can accurately
represent a tree's unique architecture. However, the required branching
data to develop these indices is hard to collect. Historically,
branching data has been collected manually. Often this process is
tedious, time-consuming, and prone to human error. These barriers can be
circumnavigated by utilizing 3D remote imaging technology, such as
terrestrial LiDAR scanning (tLiDAR). To test this, young peach trees
were scanned with 3D scanners and modeled using TreeQSM. This allowed us
to collect branching data with which to calculate BDi values.
Statistical analyses of BDi measurements from the 4 young trees will
create a methodological pipeline with which mature and complex trees
architectures may be simulated. These BDi values, either in young or
adult trees, will be used to better phenotype trees' architecture and to
better select trees for further breeding and selection (i.e. future
genomic studies - GWAS and novel QTL identification). Keywords: Plant
Breeding, Computational Biology, Phenomics, Phenotyping, Bioinformatics,
3D modelling, tLiDAR%
\end{abstract}%



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\verb`essoar.10508842.1.docx`  available at \url{https://authorea.com/users/531418/articles/597639-3d-phenotyping-of-peach-tree-canopy-architecture-using-terrestrial-laser-scanning}

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