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\begin{document}

\title{Leaf to grains: predicting multiple morpho-physiological leaf to grain
yield traits in sorghum from leaf-level hyperspectral reflectance}



\author[1]{Anuradha Singh}%
\author[1]{Brandon Webster}%
\author[1]{Linsey Newton}%
\author[1]{Addie Thompson}%
\affil[1]{Affiliation not available}%


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  \date{May 24, 2024}


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\begin{abstract}
Enhancing photosynthesis for increased sorghum grain yield has become a
key focus in sorghum breeding efforts. Phenotyping, involving the
measurement of various morpho-physiological and physical traits
associated with photosynthesis and grain yield, is a time-intensive
process. However, the potential of non-invasive leaf-level hyperspectral
imaging to swiftly detect plant performance, optimizing photosynthesis
and grain yield, is promising. This study aimed to evaluate the
feasibility of utilizing hyperspectral reflectance in the 350--950 nm
range for the rapid estimation of these traits in intact sorghum leaves.
Multiple machine learning regression algorithms were developed using
leaf-level hyperspectral reflectance data from nearly 400 sorghum
accessions within an association panel. The best-performing prediction
models were then considered as potential methods for constructing a
prediction model targeting multiple other physiological and yield traits
in sorghum accessions. The results indicate that this approach enables
the early detection of leaf photosynthetic and yield traits through
leaf-level hyperspectral reflectance without the need for a full-range,
high-cost leaf spectrometer.%
\end{abstract}%



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\textbf{Anuradha Singh\textsuperscript{1}} , Brandon
Webster\textsuperscript{2}, Linsey Newton\textsuperscript{1}, Addie
Thompson\textsuperscript{1,3}

\textsuperscript{1}Department of Plant, Soil and Microbial Science,
Michigan State University, East Lansing, MI 48824, USA

\textsuperscript{2}Department of Plant Biology, Michigan State
University, East Lansing, MI 48824, USA

\textsuperscript{3}Plant Resilience Institute, Michigan State
University, East Lansing, MI 48824, USA

*Corresponding author: thom1718@msu.edu

ORCiD - Singh: 0000-0001-9714-9095

\textbf{Keywords:} Photosynthesis, Grain yield, Hyperspectral imaging,
Machine learning regression, Sorghum

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