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\begin{document}

\title{Single plant detection from field-based drone images using deep learning}



\author[1]{Piyush Pandy}%
\author[1]{Daniel R Kick}%
\author[1]{Norman Best}%
\author[1]{Jacob D Washburn}%
\affil[1]{USDA-ARS}%


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  \date{June 06, 2024}


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\begin{abstract}
Plant phenotyping using images from unoccupied aerial systems (UAS) is
typically limited to trait acquisition at the plot level, generally
accompanied by the manual delineation of plot boundaries. However,
phenotyping of natural populations, mutant populations, and segregating
germplasm are difficult or impossible without plant level phenotypes.
Plant level boundary delineation is complicated by many factors and
manual delineation is impossible in even relatively small field
experiments. We present a method for using object detection models on
raw UAS images followed by the ``projection'' of the resulting bounding
boxes onto the orthophoto for geolocation. The method is able to detect
individual maize plants in UAS images, create accurate bounding boxes
around these plants on the orthophoto via differential
orthorectification, and extract individual plant heights from the
Digital Surface Model (DSM) as well as spectral indices like Normalized
Difference Vegetation Index (NDVI) values from orthophotos. For
verification, we compared the heights obtained from this method to the
heights obtained by manually drawing the bounding box for each plant and
calculated the precision. The method performs with accuracies as high as
0.9 depending on the growth stage and other factors. We expect this
accuracy can be increased with larger training sets and further methods
development.%
\end{abstract}%



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