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\begin{document}

\title{UAV-Based Thermal Imagery for Detecting Varietal Differences in Cotton}



\author[1,1]{Sebastian Calleja}%
\author[1,1]{Emmanuel Gonzalez}%
\author[1,1]{Duke Pauli}%
\affil[1]{University of Arizona}%


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  \date{June 01, 2024}


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\begin{abstract}
Agricultural water resources are threatened by climatic variability and
increased competition for available freshwater resources. In order to
mitigate the effect of climate change on cotton production, breeders are
increasing their efforts on improving drought tolerance in this
essential fiber crop. To achieve this, effective screening of diverse
germplasm is needed to identify useful genetic variation that can be
utilized for crop improvement. Within the last decade, unmanned aerial
vehicles (UAVs) have led to the ability to quickly and reliably image
large areas while simultaneously decreasing temporal effects associated
with a large time window for data collection. This technology allows
researchers to scale their phenotyping efforts, enabling studies that
utilize mapping and monitoring efforts such as plant water stress
detection. In this study, we used UAV-based thermal imagery to screen a
diverse population of over 350 different genotypes of cotton in order to
locate varieties that exhibit cooler canopies. This diversity panel was
grown under two contrasting levels of irrigation, well-watered and
water-limited, with data collection flights occurring weekly for three
months during the season. The thermal images were clipped to plot
boundaries, soil and plant pixels were segmented, and average
temperatures were extracted to identify potential drought tolerant
varieties. The objectives of this study were to (i) demonstrate that
UAV-based thermal imagery, along with our calibration methods, can be
used to render accurate plant canopy temperature values and (ii)
identify cotton genotypes that outperform others in a drought-stressed
environment.%
\end{abstract}%



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