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\begin{document}

\title{Remote Phenotyping Strategies for Sunflower Flowering Assessments at
Corteva Agriscience}



\author[1]{Leonardo Volpato}%
\author[1]{Sara Tirado Tolosa}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
The flowering date of sunflowers is a crucial trait that significantly
influences crop management practices and product placement. This trait
can be measured by counting the number of days from planting until 50\%
of plants in a given research plot have reached flowering at R5
developmental growth stage. Traditional ground methods for data
collection are labor-intensive and subjective, requiring field
scientists to manually estimate and record plots with 50\% flowering
every 1-2 days. This approach not only consumes considerable time but
also potentially overlooks valuable information related to flowering
rates and duration. To address these challenges, we leveraged UAVs
(Unmanned Aerial Vehicles), which allow for surveying a field in a short
span of time, to model flower counts over time and predict the date when
the plot reached 50\% flowering. The method developed employs a deep
learning model trained to detect yellow sunflower heads from UAV imagery
and modeling these counts over time using a logistic function to
estimate the 50\% flowering date. With this method, flowering date was
precisely estimated with high correlation relative to the ground
measurements (r = \textgreater{}0.92) across experiments and locations.
An increase in heritability for the remote sensing trait was also
observed relative to the ground trait, and more importantly we were able
to gain additional insights into flowering rates and duration. This
innovative approach offers a promising avenue for enhancing the
efficiency and accuracy of sunflower phenotyping.%
\end{abstract}%



\sloppy


\emph{\textbf{Leonardo Volpato\textsuperscript{1}}, and Sara Tirado
Tolosa\textsuperscript{\textbf{1}}}

\emph{\textsuperscript{1}Seed Product Development, Corteva Agriscience,
Johnston, USA}

\textbf{ORCiD:} 0000-0003-1119-0615

\textbf{Keywords:} \emph{Helianthus annuus} , Machine learning, Drone
imagery, HTP, Crop management, Precision phenotyping

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