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\begin{document}

\title{Eye in the Sky: Hyperspectral Imaging for Sustainable Nitrogen
Management in Vegetables}



\author[1]{Taqdeer Gill}%
\author[1]{Philip A. Townsend}%
\author[1]{Yi Wang}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
Snap beans and kidney beans are poor nitrogen fixers and need nitrogen
(N) fertilizer. However, excessive application of N leads to groundwater
contamination. The traditional way of measuring crop nitrogen status is
destructive and time-consuming. The objective of this study was to
develop a tool that accurately predict the real-time crop nitrogen
status and the end-of season yield for optimizing fertilizer management.

The field trial was conducted in 2022 and 2023. Eight nitrogen
treatments were applied at 22 kg ha\textsuperscript{-1}, 56 kg
ha\textsuperscript{-1}, 84 kg ha\textsuperscript{-1}, 112 kg
ha\textsuperscript{-1}, 140 kg ha\textsuperscript{-1}, 168 kg ha-1, 196
kg ha\textsuperscript{-1}, 224 kg ha\textsuperscript{-1} to three kidney
beans cultivars. Six nitrogen treatments were applied at 22 kg
ha\textsuperscript{-1}, 56 kg ha\textsuperscript{-1}, 84 kg
ha\textsuperscript{-1}, 112 kg ha\textsuperscript{-1}, 140 kg
ha\textsuperscript{-1}, 168 kg ha\textsuperscript{-1} to two snap beans
cultivars. Hyperspectral images (400 nm to 2500 nm) were collected on a
weekly basis. Top twenty bands along with genotype (cultivars),
environmental factors (temperature, precipitation, growing Degree Days),
management factors (nitrogen rate, days after planting, irrigation) were
used to train different machine learning algorithms including linear
regression, random forest, XG Boost, support vector machine and
k-nearest neighbors for predicting the nitrogen status and the final
yield.

Our results indicated that top twenty bands along with GEM performed the
best for predicting final yield (R\textsuperscript{2} as high as 0.82
and RMSE as low as 1.6). Our study demonstrated the potential capacity
of hyperspectral imaging and machine learning models to estimate crop
yield and nitrogen status.%
\end{abstract}%



\sloppy


\emph{\textbf{Taqdeer Gill\textsuperscript{1}}, Philip A.
Townsend\textsuperscript{\textbf{1}}, Yi Wang\textsuperscript{1}}

\emph{\textsuperscript{1}University of Wisconsin-Madison, Madison,
United States of America}

\textbf{ORCiD: 0009-0002-7030-5312}

\textbf{Keywords:} Hyperspectral Imaging, Nitrogen Management, Yield
Prediction, In-Season Nitrogen Prediction

\par\null

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