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\begin{document}

\title{Tracking dynamic changes of leaves in response to nutrient availability
using an open-source cloud-based phenotyping system (OPEN Leaf)}



\author[1]{Landon G. Swartz}%
\author[2]{Suxing Liu}%
\author[1]{Drew Dahlquist}%
\author[3]{Emily S Walter}%
\author[3]{Sam Mcinturf}%
\author[2]{Alexander Bucksch}%
\author[1]{David G Mendoza-Cozatl}%
\affil[1]{University of Missouri-Columbia}%
\affil[2]{University of Georgia}%
\affil[3]{C.S. Bond Life Sciences Center}%


\vspace{-1em}



  
  \date{June 08, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
(250 words) Micronutrients, such as iron, zinc, and sulfur, play a vital
role in both plant and human development. Understanding how plants sense
and allocate nutrients within their tissues may offer different venues
to develop plants with high nutritional value. Despite decades of
intensive research, more than 40\% of genes in Arabidopsis remain
uncharacterized or have no assigned function. While several resources
such as mutant populations or diversity panels offer the possibility to
identify genes critical for plant nutrition, the ability to consistently
track and assess plant growth in an automated, unbiased way is still a
major limitation. High-throughput phenotyping (HTP) is the new standard
in plant biology but few HTP systems are open source and user friendly.
Therefore, we developed OPEN Leaf, an open source HTP for hydroponic
experiments. OPEN Leaf is capable of tracking changes in both size and
color of the whole plant and specific regions of the rosette. We have
also integrated communication platforms (Slack) and cloud services
(CyVerse) to facilitate user communication, collaboration, data storage,
and analysis in real time. As a proof-of-concept, we report the ability
of OPEN Leaf to track changes in size and color when plants are growing
hydroponically with different levels of nutrients. We expect that the
availability of open source HTP platforms, together with standardized
experimental conditions agreed by the scientific community, will advance
the identification of genes and networks mediating nutrient uptake and
allocation in plants.%
\end{abstract}%



\sloppy


\textbf{Tracking dynamic changes of leaves in response to nutrient
availability using an open-source cloud-based phenotyping system (OPEN
\emph{Leaf)}}

\textbf{Landon G. Swartz\textsuperscript{a}} , Suxing
Liu\textsuperscript{b}, Drew Dahlquist\textsuperscript{a}, Emily S.
Walter\textsuperscript{c}, Sam McInturf\textsuperscript{c}, Alexander
Bucksch\textsuperscript{b}, David G. Mendoza-Cozatl\textsuperscript{a,c}

\textsuperscript{a} Department of Electrical Engineering and Computer
Science, University of Missouri-Columbia, USA;
\textsuperscript{b}Department of Plant Biology and Institute of
Bioinformatics, University of Georgia, Athens, U.S.A;
\textsuperscript{c} Division of Plant Sciences, C.S. Bond Life Sciences
Center, University of Missouri-Columbia, U.S.A.

\textbf{ABSTRACT (250 words)}

Micronutrients, such as iron, zinc, and sulfur, play a vital role in
both plant and human development. Understanding how plants sense and
allocate nutrients within their tissues may offer different venues to
develop plants with high nutritional value. Despite decades of intensive
research, more than 40\% of genes in Arabidopsis remain uncharacterized
or have no assigned function. While several resources such as mutant
populations or diversity panels offer the possibility to identify genes
critical for plant nutrition, the ability to consistently track and
assess plant growth in an automated, unbiased way is still a major
limitation. High-throughput phenotyping (HTP) is the new standard in
plant biology but few HTP systems are open source and user friendly.
Therefore, we developed \emph{OPEN Leaf,} an open source HTP for
hydroponic experiments. \emph{OPEN Leaf} is capable of tracking changes
in both size and color of the whole plant and specific regions of the
rosette. We have also integrated communication platforms (Slack) and
cloud services (CyVerse) to facilitate user communication,
collaboration, data storage, and analysis in real time. As a
proof-of-concept, we report the ability of \emph{OPEN Leaf} to track
changes in size and color when plants are growing hydroponically with
different levels of nutrients. We expect that the availability of open
source HTP platforms, together with standardized experimental conditions
agreed by the scientific community, will advance the identification of
genes and networks mediating nutrient uptake and allocation in plants.

\textbf{Keywords:} High-throughput phenotyping, Cloud-based, CyVerse,
Plant Nutrition

\textbf{INTRODUCTION}

Predictive biology, or predicting biological outcomes from a known
input, is central to understanding the genome-to-phenome
relationship\textsuperscript{1}. However, the phenome is a complex
combination of the result of environmental conditions and the ability of
the organism to adapt to environmental changes\textsuperscript{2,3}. In
the case of plants, predictive biology requires a deep understanding of
its genetics and its response to environmental cues, such as light
intensity, water availability, and other organisms\textsuperscript{4,5}.
Therefore, the prediction of plant responses to changes in environmental
conditions has lagged compared to other advances in the understanding of
other plant behaviors. The consistent collection of reproducible data at
the ``-omic'' and environmental level are vital towards addressing this
issue.

A drastic decline in cost has allowed for large amounts of genomic and
environmental data to be collected, yet major bottlenecks still stand in
integrating, sharing, and analyzing these datasets. For example, methods
characterizing genomes, such as DNA sequencing, have advanced far more
quickly than methods for phenomes\textsuperscript{6,7}. Plant
phenotyping at scale is costly and inaccessible to the majority of plant
researchers\textsuperscript{8,9.10}. However, many components for
high-throughput phenotyping, such as sensors and computer vision, have
become cheaper and more accessible for development of phenotyping
platforms. These platforms can collect information on
roots\textsuperscript{11,12} and shoots\textsuperscript{13,14} in
greenhouses\textsuperscript{15,16} to whole
fields\textsuperscript{17,18}. However data management and cost still
stand as major limitations in reproducing abiotic stresses from water
and nutrient deficiency\textsuperscript{7,10,19,20}.

In response, we propose OPEN Leaf {[}Open PhENotyper{]}. OPEN Leaf is
designed to be an open-source plant phenotyping system that tracks
rosette growth in Arabidopsis by color and area. This system was built
using commercially available materials and uses a high resolution RGB
camera and a track system with user-defined positions to capture dynamic
changes in Arabidopsis rosette growth over time. Furthermore,
complications due to the COVID-19 pandemic, spurred the integration of
remote communication to observe data collection in real time with OPEN
Leaf. Overall, OPEN Leaf is a modular, scalable, and cloud-based system
that will enable researchers across the globe to share and process
reproducible experiments in predictive biology.

\textbf{MATERIALS AND METHODS}

\textbf{2.1 OPEN Leaf}

OPEN Leaf's hardware is inexpensive and can be acquired from several
commercial sources. The frame was built using the open-source T-slot
aluminum beams (80/20 LLC., Columbia City, IN, USA) and can be bought
pre-cut or bought in bulk then cut down to size. The lead-screw track
system is a commercial product called C-Beam Linear Actuator
(openbuilds.com). The system includes the C-beam track, the gantry
plate, and a NEMA23 stepper motor. The bundle comes with detailed
instructions from OpenBuilds and designed for extended heavy-duty use. A
bracket used to convert from the 80/20 beams to the OpenBuild's track
was 3D printed on a Prusa i3 MK3 (Prusa Research, Partyz\selectlanguage{ngerman}ánská, Czech
Republic). The camera used was the Allied Vision Mako G-503 (Allied
Vision, Stadtroda, Germany), a gigabit ethernet RGB camera that can
capture high-resolution images.

An open-source C\# desktop application is used to control the machine.
The program has the ability to control several systems in parallel;
however, since each system requires an ethernet port, this can be a
bottleneck. The track system uses the open-source GRBL CNC mill g-code
parser to drive the lead screw system. The desktop application sends
manual g-code through an Arduino (arduino.cc), a hobbyist
microcontroller, to travel to capture images.

\textbf{2.2 CyVerse and Slack Integration}

CyVerse (CyVerse.org) is an NSF-funded cloud-based system designed to
revolutionize science with data-driven discovery for Life Sciences
research with training for scientists in mind. We provide the choice of
integrating the image processing with CyVerse or allowing researchers to
use another method. CyVerse offers many benefits for cloud storage,
including data management and sharing. A desktop application named
CyberDuck uses a command-line interface for communicating with CyVerse.
A python script is set with user credentials to send command-line
commands to automate the process of synchronization data in the remote
directories of CyVerse.

Slack is a prominent communications platform that provides tool for
connecting workspaces across the globe. We use a ``bot'' account to
instantly communicate with the OPEN Leaf system. We can remotely
determine if the system is functioning and see the most recently
collected series of images.

\textbf{2.3 Image Analysis}

The image analysis is hosted on CyVerse to allow for automated
processing. All images captured on the OPEN Leaf system are consistent.
Therefore, the image processing script requires no human inputs,
eliminating the possibility for human bias. The image processing script
produces an excel sheet of results that include traits of whole rosette
and leaves such as: area, major axis, minor axis, and color distribution
clusters. The area is measured in pixels. Major and minor axis are the
vertical and horizontal height of the plant in pixels. The four most
dominant hex value colors extracted from the plant are provided as a
percentage out of 100\%.

\textbf{RESULTS AND DISCUSSION}

\textbf{3.1 Overview}

A multi-faceted approach for acquiring and processing data in a low-cost
and accessible manner is paramount towards predictive biology.
Understanding the phenome to genome relationship requires discovering
new genes through uncharacterized phenotypes (Figure 1a). Using several
open source and commercial products, such as Allied Vision cameras and
the hobbyist microcontroller Arduino, OPEN Leaf characterizes phenotypes
at a macro and micro scale by quantifying growth by area and color of
the whole plant and specific leaves. OPEN Leaf was designed to be
affordable, accessible, and open source from the beginning. Images can
be synced to the cloud service \emph{CyVerse} to perform image
processing. The communication platform \emph{Slack} has integration for
remote communication with the machine (Figure 1b).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. (a) Uncharacterized phenotypes can be characterized with OPEN
Leaf through a high-resolution camera. (b) Slack integration allows for
remote work with the system

\textbf{3.2 Whole leaf analysis}

OPEN Leaf captures whole images of plant to track the overall
development of the plant dynamically through time. The two most
important characteristics to observe in plant development, specifically
for nutrient uptake, is the change in area and
color\textsuperscript{14}(Figure 2). These approaches provide many
valuable insights into plant development and the characterization of
phenotypes. However, dynamic detail that occur on leaf level are often
overlooked by only observing plant as a whole. For example, iron
deficiency displays itself primary in younger leaves, and therefore,
when observing the whole plant, area changes very little since older
leaves are consistent in growth and color is minimally
impacted\textsuperscript{21}. Ergo, it is important to observe growth of
leaves independently of the entire plant.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Whole plant analysis shows important macro trends of growth
across the whole plant by area and color

\textbf{3.3 Leaf specific analysis}

Leaf development varies wildly throughout plant development depending on
set of true leaves the leaf is (Figure 3). OPEN Leaf allows for the
manual tracking of these leaves to observe the dynamic changes in leaves
under nutrient stress across all age ranges. Moreover, color changes,
such as the emergence of chlorosis, are much more pronounced at a leaf
level. This resolution allows for a deep understanding of the complex
relationship of mobile nutrients and growth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Leaf specific analysis provide new insights to nutrient
deficiency phenotypes by observing the changes of area and color one
leaf at a time

\textbf{CONCLUSION}

OPEN Leaf addresses many of the current limitations and inconveniences
of HTP systems in predictive biology. OPEN Leaf is open-source and
accessible, characterizes abiotic stress responses within hydroponics,
integrated data management through\emph{CyVerse} , and automated image
processing. Many high-resolution HTP systems are commercial and outside
of budgets for most labs. OPEN Leaf is \$1000 and entirely open source.
OPEN Leaf characterizes abiotic stress responses at a whole plant and
leaf-specific to aid in understanding the genetically complex systems
involved in stress response. Many other open-source HTP systems have no
method for data management and processing. OPEN Leaf utilizes
\emph{CyVerse} to both manage and process data; allowing labs across the
globe to participate in collaborative research. Future projects in the
OPEN series includes increasing throughput of OPEN Leaf and a root
phenotyper with similar OPEN principles.

\textbf{DATA AVAILABILITY STATEMENT}

Image data available at CyVerse link provided at the following link:
https://de.cyverse.org/data/ds/iplant/home/dgmendoz/DMC\_phenotyping/DMC\_photos/SRP/18Jan2021\_thin.
OPEN Leaf Controlling Software available at a Github repository:
https://github.com/LandonSwartz/Cyberbear/tree/Single-Row.

\textbf{ACKNOWLEDGMENTS}

This research was supported in part by the US National Science
Foundation (MCB-1818312 and IOS- 1734145 to D. M. C.), USDOE ARPA-E
ROOTS Award Number DE-AR0000821, and the NSF CAREER Award No. 1845760 to
A.B. Any opinions findings, and conclusions or recommendations expressed
in this material are those of the author(s) and do not necessarily
reflect those of ARPA-E or the National Science Foundation.

\textbf{REFERENCES}

{[}1{]} Lopatkin, A and Collins J. ``Predictive Biology: Modelling,
Understanding and Harnessing Microbial Complexity''. Nature Reviews
Microbiology, 18, 9, 507-520 (2020).

{[}2{]} Des Marais, D., Hernandez K., and Juenger T.
``Genotype-by-Environment Interaction and Plasticity: Exploring Genomic
Responses of Plants to the Abiotic Environment'' Annu. Rev. Ecol. Evol.
Syst. (2013).

{[}3{]} Buckner, E., Madison, I., Chou, H., Matthiadis, A., Melvin C.
Sozzani, R., Williams, C., Long, T. ``Automated Imaging, Tracking, and
Analytics Pipeline for Differentiating Environmental Effects on Root
Meristematic Cell Division'' Frontiers in Plant Science, 10, 1487
(2019).

{[}4{]} Lundberg, D., Lebeis, S., Paredes, S., Yourstone, S., Gehring,
J., Malfatti S., Tremblay, J., Engelbrekston, A., Kunin, V., Del Rio,
T., Edgar, R., Eickhorst, T., Ley, R., Hugenholtz, P., Tringe S.,
Dangle, J. ``Defining the core Arabidopsis thaliana root microbiome''
Nature, 488, 7409, 86-90 (2012).

{[}5{]} Legris, M., Klose, C., Burgie, E., Rojas C., Neme, M.,
Hiltbrunner A., Wigge P., Schafer, E., Vierstra, R., Casal, J.
``Phytochrome B integrates light and temperature signals in
Arabidopsis'' Science, 6314, 897 (2016).

{[}6{]} Moore, C., Johnson, L., Livny , M., Broman, K., Spalding, E.,
``High-Throughput Computer Vision Introduces the Time Axis to a
Quantitative Trait Map of a Plant Growth Response'' Genetics (2013).

{[}7{]} Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J., Batchelor
W., Xiong, L., Yan, J. ``Crop Phenomics and High-Throughput Phenotyping:
Past Decades, Current Challenges, and Future Perspectives'' Molecular
Plant, 13, 2, 187-214 (2020)

{[}8{]} Shendure J. and Ji H., ``Next-generation DNA sequencing'' Nature
Biotechnology (2008).

{[}9{]} Jackson, S., Iwata, A., Lee, S., Schmutz, J., Shoemaker, R.
``Sequencing crop genomes: approaches and applications.'', The New
Phytologist, 191, 4, 915-925. (2011).

{[}10{]} Reynolds, D., Baret, F., Welcker, C., Bostrom, A., Ball, J.,
Cillini, F., Lorence, A., Chawade, A., Khafif, M., Noshita, K.,
Mueller-Linow, M., Zhou, J., Tardieu, F. ``What is cost-efficient
phenotyping? Optimizing costs for different scenarios'', The 4th
International Plant Phenotyping Symposium, 282, 14-22 (2019).

{[}11{]} Le Mari\selectlanguage{ngerman}é, C., Kirchgessner, N., Marschall, D., Walter, A.,
Hund, A. ``Rhizoslides: paper-based growth system for non-destructive,
high throughput phenotyping of root development by means of image
analysis'', Plant Methods, 10, 1, 13 (2014).

{[}12{]} Mathieu, L., Lobet, G., Tocquin, P., Périlleux, C.,
``'Rhizoponics': a novel hydroponic rhizotron for root system analyses
on mature Arabidopsis thaliana plants'', Plant Methods, 11, 1, 3 (2015).

{[}13{]} Flood, P., Kruijer, W., Schnabel, S., Jalink, H., Snel, J.,
Harbinson, J., Aarts, M., ``Phenomics for photosynthesis, growth and
reflectance in Arabidopsis thaliana reveals circadian and long-term
fluctuations in heritability'' Plant Methods, 12, 14 (2016).

{[}14{]} Awlia, M., Nigro, A., Fajkus, J., Schmoeckel, S., Negrão, S.,
Santelia, D., Trtílek, M., Tester, M., Julkowska, M., Panzarova, K.,
``High-Throughput Non-destructive Phenotyping of Traits that Contribute
to Salinity Tolerance in Arabidopsis thaliana'', Frontiers in Plant
Science, 7, 1414. (2016).

{[}15{]} van der Heijden, G., Song, Y., Horgan, G., Polder, G., Dieleman
A., Bink, M., Palloix, A., van Eeuwijkm F., Glasbey, C. ``SPICY: towards
automated phenotyping of large pepper plants in the greenhouse'',
Functional Plant Biology, 39, 11 (2012).

{[}16{]} Yang, W., Guo, Z., Duan, L., Chen, G., Jiang, N., Fang, W.,
Feng H., Xie, W., Lian, X., Wang, G., Luo, Q., Zhang, Q., Liu, Q.,
Xiong, L. ``Combining high-throughput phenotyping and genome-wide
association studies to reveal natural genetic variation in rice'' Nature
Communications, 5, 1, 5087 (2014).

{[}17{]} Sadeghi-Tehran, P., and Sabermanesh K. ``Automated Method to
Determine Two Critical Growth Stages of Wheat: Heading and Flowering''
Frontiers in Plant Science, 8, 252 (2017).

{[}18{]} Maes W., and Steppe K. ``Perspectives for Remote Sensing with
Unmanned Aerial Vehicles in Precision Agriculture'', Trends in Plant
Science, 24, 2, 152-164 (2019).

{[}19{]} Billiau K., Sprenger, H., Schudoma, C., Walther, D., Kohl, K.,
``Data management pipeline for plant phenotyping in a multisite
project'', Functional Plant Biology, 39, 11, 948-957 (2012).

{[}20{]} Tsaftaris, S., and Scharr H. ``Sharing the Right Data Right: A
Symbiosis with Machine Learning'', Trends in Plant Science, 24, 2,
99-102 (2019)

{[}21{]} Zhang, X., Zhang, D., Sun, W., and Wang, T. ``The Adaptive
Mechanism of Plants to Iron Deficiency via Iron Uptake, Transport, and
Homeostasis.'' Int. J. Mol. Sci.\emph{,} 20\emph{,} 2424 (2019).

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