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\begin{document}

\title{Satellite remote sensing of soybean seed composition with AI}



\author[1]{Kamila Dilmurat}%
\author[1]{Vasit Sagan}%
\author[1]{Kristen Rhodes}%
\author[2]{Felix Fritschi}%
\affil[1]{Geospatial Institute, Saint Louis University, St. Louis, MO 63108, USA}%
\affil[2]{Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA}%


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  \date{June 02, 2024}


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\begin{abstract}
Preharvest seed composition estimation using satellite imaging provides
critical information for food security planning and management at a
regional scale. As one of the staple crops, soybean plays important role
in U.S. economic development. Estimating soybean seed composition is the
precondition for improving seed quality and meal content at scale,
therefore, maintaining U.S. soy competence in international markets.
Traditionally, soybean seed compositions are measured after harvest via
wet chemistry analysis, which is time-consuming and expensive. This
study presents very first, to the best of our knowledge, satellite
remote sensing of soybean seed composition. We demonstrate that
WorldView-3 satellite imagery and machine/deep learning is powerful tool
to predict seed composition from standing crops.%
\end{abstract}%



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