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\begin{document}

\title{Maize Yield Prediction Based on Multi-Modality Remote Sensing
and Deep Neural Network Models in Nitrogen Management Practice Trials}



\author[1]{Claudia Aviles Toledo}%
\author[1]{Melba Crawford}%
\author[1]{Tony Vyn}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\begin{abstract}
High spatial resolution remote sensing and machine learning have
improved the accuracy and affordability of high-throughput phenotyping.
This paper presents an innovative approach to yield prediction,
leveraging multimodal networks and integrating remote sensing data from
two sensing technologies. We explore the synergy among different types
of remote sensing data, meteorological data, and management practices
employing advanced machine learning techniques to enhance accuracy and
reliability in yield predictions.

This study focuses on three experiments, one in 2021 and two in
different environments with distinct management practices in 2022.
Hyperspectral, LiDAR and weather-related features served as initial
inputs to the LSTM recurrent neural network-based yield prediction
models; management practices related categorical variables were
concatenated after the time series output from the attention network. In
each experiment, 80\% of the data was used for training the model and
20\% for testing, investigating three deep learning networks:

\begin{enumerate}
\tightlist
\item
  Traditional vanilla stacked LSTM network.
\item
  Stacked LSTM Network with a temporal attention mechanism.
\item
  Multi-modal network for the different remote sensing modalities.
\end{enumerate}

The attention weights of each time-step were evaluated to determine the
importance of each date;

All models produced good predictions, but the attention mechanism
coupled with the multi-modality network showed the effectiveness of
combining multimodal remote sensing data optimally throughout the season
and deep learning algorithms in optimizing agricultural decision-making
processes. The Attention networks also provided increased
interpretability over the growing season, showing flowering time to be
the most critical time for the models, which is consistent with
field-based trials.%
\end{abstract}%



\sloppy


\emph{\textbf{Claudia Aviles Toledo\textsuperscript{1}}, Melba
Crawford\textsuperscript{1,2}, Tony Vyn\textsuperscript{2}}

\emph{\textsuperscript{1}Lyles School of Civil Engineering, Purdue
University, West Lafayette, IN 47907, USA}

\emph{\textsuperscript{2}Department of Agronomy, Purdue University, West
Lafayette, IN 47907, USA}

\textbf{ORCiD:} 0009-0009-2013-3293

Keywords: Hyperspectral, LiDAR, nitrogen management practices,
categorical variables, attention mechanisms, multi-modal networks.

\par\null

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