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\begin{document}

\title{Integration of multi-modal proximal remote sensing data for improved prediction of agronomically important crop traits}



\author[1]{Erin Farmer}%
\author[2]{Ruyu Yan}%
\author[2]{Peter Michael}%
\author[3]{Nick Lepak}%
\author[4]{Cinta Romay}%
\author[1,3,4]{Ed Buckler}%
\author[5]{Noah Snavely}%
\author[6]{Ying Sun}%
\author[1]{Kelly Robbins}%
\author[7]{Joe Gage}%
\author[2]{Abe Davis}%
\author[1]{Michael Gore}%
\affil[1]{Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University}%
\affil[2]{Department of Computer Science, Cornell University}%
\affil[3]{Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health}%
\affil[4]{Institute for Genomic Diversity, Cornell University}%
\affil[5]{Cornell Tech, Cornell University}%
\affil[6]{Soil and Crop Sciences Section, School of Integrative Plant Science, Cornell University}%
\affil[7]{Department of Crop and Soil Sciences, North Carolina State University}%


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  \date{May 17, 2024}


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\begin{abstract}
Recent advancements in proximal remote sensing have increased the
spatial and temporal resolution of data collection, as well as the
availability of these technologies for applications to precision
agriculture. These sensors have allowed the collection of new and large
quantities of data, which have been used to successfully determine
phenotypes and parametrize crop growth models. So far, these data
streams have been mostly used separately, though they contain unique
structural, spatial, and spectral information. Thus, this research aims
to integrate these disparate data sources to improve estimations of
agronomically important crop traits. In this study, we examine two
high-throughput and relatively inexpensive remote platforms: unoccupied
ground vehicles (UGV) and unoccupied aerial vehicles (UAV). Data were
collected on maize hybrids from the Genomes to Fields initiative over 5
years, from 2018 to 2022, in Aurora, NY. We used ground rovers to
collect lidar scans, which were converted to point clouds, to construct
the three-dimensional sub-canopy architecture of maize plants.
Multispectral sensors, covering red, green, blue, red-edge, and near
infrared (NIR) were deployed on a UAV platform to characterize maize
canopies. Machine learning methods, including autoencoders, will be used
to extract latent phenotypes from the lidar point clouds and
multispectral images. Ultimately, these will be used to predict manually
measured traits, such as yield, in order to compare the prediction
accuracies of models using these measurements separately and jointly. ~%
\end{abstract}%



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