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\begin{document}

\title{Flower Mapping in Grasslands with Drones and Deep Learning}



\author[1]{Johannes Gallmann}%
\author[2]{Beatrice Schüpbach}%
\author[2]{Katja Jacot}%
\author[2]{Matthias Albrecht}%
\author[2]{Jonas Winizki}%
\author[1]{Helge Aasen}%
\affil[1]{ETH Zürich}%
\affil[2]{Agroscope Location Reckenholz}%


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  \date{June 16, 2024}


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\begin{abstract}
Manual assessment of flower abundance of different flowering plant
species in grasslands is a time consuming process. We present an
automated approach to determine the flower abundance in grasslands from
drone images using a deep learning (Faster R-CNN) object detection
approach, which is trained and evaluated on data of five flights and two
sites. Our deep learning network is able to identify and classify
individual flowers. The novel method allows generating spatially
explicit maps of flower abundance that meets or exceeds the accuracy of
the manually counted extrapolation method and is less labor intensive.
The results are very good for some types of flowers with precision and
recall being close to or higher than \$90\textbackslash{}
\textbackslash{}\%\$. Other flowers are detected poorly due to reasons
such as lack of enough training data, appearance changes due to
phenology or flowers being too small to be reliably distinguishable on
the aerial images. The method is able to give precise estimates of the
abundance of many flowering plant species. The collection of more
training data will allow better predictions in the future for the
flowers that are not well predicted yet. The developed pipeline can be
applied to any sort of aerial object detection problems.%
\end{abstract}%



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