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\begin{document}

\title{Out of the lab and into the field: Applications of PlantCV for analyzing
field-based images of aboveground and belowground perennial crop tissues}



\author[1]{Stella Woeltjen}%
\author[1]{Noah Fahlgren}%
\author[1]{Malia Gehan}%
\author[1]{Shayla Gunn}%
\author[1]{Haley Schuhl}%
\author[1]{Dhiraj Srivastava}%
\author[1]{Molly Hanlon}%
\author[1]{Chris Topp}%
\author[1]{Ivan Baxter}%
\author[1]{Allison Miller}%
\affil[1]{Affiliation not available}%


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  \date{May 26, 2024}


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\begin{abstract}
Advances in automated image analysis using open-source computer vision
tools, such as PlantCV, have greatly increased the throughput of
aboveground phenotyping in a variety of crop species. However, PlantCV
was largely optimized to analyze images collected under controlled
laboratory conditions, and has seldom been used to analyze images
collected under field conditions. Further, there are no known
applications of PlantCV for analyzing images collected belowground, such
as those obtained from minirhizotron imaging devices. In this study, we
demonstrated applications of PlantCV for extracting plant trait
information from aboveground and belowground images collected in two
perennial crop mapping populations. The first population was composed of
nearly 1,200 individuals of a potential perennial oilseed crop
(\emph{Silphium Integrifolium x Perfoliatum} ), and the second
population was composed of nearly 1,700 individuals of a perennial cover
crop (\emph{Trifolium ambiguum} , Kura Clover). We designed and used a
field-based imaging cart to collect overhead and profile images of
individuals from both populations in August and October, which improved
the efficiency of field-based image capture. Around the time of
aboveground image collection, belowground images of root networks were
collected using minirhizotron imaging devices. We then assessed the
application of PlantCV for measuring aboveground traits (crop canopy
area, height, leaf color and growth rates) and belowground traits (root
length and growth rates), and we explored future directions of PlantCV
for field-based image analysis of aboveground and belowground crop
tissues.%
\end{abstract}%



\sloppy


\emph{\textbf{Stella Woeltjen\textsuperscript{1}}, Noah
Fahlgren\textsuperscript{1}, Malia Gehan\textsuperscript{1}, Shayla
Gunn\textsuperscript{1}, Haley Schuhl\textsuperscript{1}, Dhiraj
Srivastava\textsuperscript{1}, Molly Hanlon\textsuperscript{1}, Chris
Topp\textsuperscript{1}, Ivan Baxter\textsuperscript{1}, Allison
Miller\textsuperscript{1}}

\emph{\textsuperscript{1}Donald Danforth Plant Science Center, Saint
Louis, MO, United States of America}

\textbf{Keywords:} PlantCV, automated image analysis, perennial crops,
Silphium, Kura Clover, minirhizotron imaging

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