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\begin{document}

\title{Three-dimensional phenotyping of peach tree crown architecture utilizing
terrestrial laser scanning}



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


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  \date{May 24, 2024}


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\begin{abstract}
Tree training systems for temperate fruit have been developed throughout
history by pomologists to improve light interception, fruit yield, and
fruit quality. These training systems direct crown and branch growth to
specific configurations. Quantifying crown architecture could aid the
selection of trees that require less pruning or that naturally excel in
specific growing/training system conditions. Regarding peaches {[}Prunus
persica (L.) Batsch{]}, access tools such as branching indices (BIs)
have been developed to characterize tree crown architecture. However,
the required branching data to develop these indices are difficult to
collect. Traditionally, branching data have been collected manually, but
this process is tedious, time-consuming, and prone to human error. These
barriers can be circumnavigated by utilizing terrestrial LiDAR (TLS) to
obtain a digital twin of the real tree. TLS generates three-dimensional
(3D) point clouds of the tree crown, wherein every point contains 3D
coordinates (x, y, z). To facilitate the use of these tools for peach,
we selected four young peach trees scanned in 2021 and 2022. These four
young trees were then modeled and quantified using the open-source
software TreeQSM. As a result, ``in silico'' branching and biometric
data for the young peach trees were calculated to demonstrate the
capabilities of TLS phenotyping of peach tree-crown architecture. The
comparison and analysis of field measurements (in situ) and in silico
branching data (BD), biometric data, and residual ground truth data were
utilized to determine the reconstructive model's reliability as a source
substitute for field measurements. Mean average deviation (MAD) when
comparing young tree height was approx. 8.2\%, with crown volume (crV)
was approx. 7.6\% across both 2021 and 2022. All point clouds of the
young trees in 2022 showed residuals \textless{} 10mm to cylinders
fitted to all branches, and mean surface coverage \textgreater{}50\%
across all branching orders.%
\end{abstract}%



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\verb`JKW_NAPPN_journal_v8_ln.docx`  available at \url{https://authorea.com/users/564522/articles/618370-three-dimensional-phenotyping-of-peach-tree-crown-architecture-utilizing-terrestrial-laser-scanning}

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