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\begin{document}

\title{Understanding the Phenotypic Variation Among the Soybean (\emph{Glycine
max} L.) Lines by Using Crop Modeling and Remote Sensing}



\author[1]{Sujata Bogati}%
\author[2]{S Bogati}%
\author[2]{S E Shafer}%
\author[3]{I Olaniyi}%
\author[3]{J Jung}%
\author[2]{D R Wang}%
\affil[1]{Affiliation not available}%
\affil[2]{Department of Agronomy, Purdue University}%
\affil[3]{Lyles School of Civil Engineering, Purdue University}%


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  \date{May 21, 2024}


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\begin{abstract}
ORCiD: {[}\url{https://orcid.org/0000-0003-0655-2343}{]} Increasing
weather variability is affecting the overall productivity of
agriculture. In this scenario, current crop improvement science is
essential to improve productivity while retaining the quality of plant
products. There has long been an interest in using process-based
modeling to examine the interaction between environment and genotype.
The methodological challenges to better predict how various
environmental conditions may impact novel genotypes and it has been a
fundamental barrier to model parameterization. Thus, a phenotypic
campaign was conducted to collect a comprehensive physiological dataset
from a panel of 25 genotypes (including both breeder panel and diversity
panel) in the summer of 2022. Additionally, an unmanned aerial vehicle
(UAV) was used to gather remote sensing data. The red-green-blue (RGB)
3D point cloud, NDVI (Normalize difference vegetation Indices), and
LIDAR (Light detection and ranging) were also used to identify the trait
variations among the genotypes. The data is being analyzed to explain
the physiological and phenotypic trait differences. The outcome of this
project would help to develop a genetically informed, realistic soybean
model. Finally, it will help breeders and growers in locating
high-yielding cultivars for the appropriate geographical areas.%
\end{abstract}%



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