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\begin{document}

\title{Automated high throughput plant phenotyping to assess sunflower planting
dates}



\author[1]{Tshegofatso Mbere}%
\author[2]{Dirk Swanevelder}%
\author[3]{Rian Pierneef}%
\author[1]{Nicky Creux}%
\affil[1]{Department of Plant and Soil Sciences, FABI, University of Pretoria}%
\affil[2]{Biotechnology platform, Agricultural Research Centre}%
\affil[3]{Bioinformatics unit, University of Pretoria}%


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  \date{May 19, 2024}


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\begin{abstract}
South Africa is dependent on dryland farming, causing crops to be sown
only once the rainy season begins. With climate change altering weather
patterns, unpredictable rainy seasons may lead to cash crops such as
maize and soybean being planted later, which makes them susceptible to
extreme temperatures and drought. Farmers often switch to planting
sunflower (\emph{Helianthus anuus} L.) under these conditions because of
its shorter rotation time and hardier nature. For this reason, sunflower
tends to be planted outside of its optimal planting window. To
understand the effects of different planting dates on sunflower growth
and development, a planting date trial was established in Potchefstroom
under the Phenospex FieldScan system. Low-and high-density monthly
plantings were sown from October to March and scanned three times a day
(RGB, NIR, Laser). Values are extracted to calculate plant growth and
health indices over the season for each planting date. The aim of this
project is to develop a Shiny application that can analyse the Phenospex
data outside of the platform. Phenospex software is expensive and
impractical to use in the South African context with unstable
infrastructure because it requires the sever to be on in one location
for analysis in a different location. The Shiny app will allow Phenospex
data to be analysed offline, offering more flexibility in an African
setting.%
\end{abstract}%



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