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\begin{document}

\title{High-frequency \emph{in situ} root phenotyping achieved by automated
Minirhizotron image acquisition and analysis~~~~~~~~~~~~}



\author[1]{Zhou Kaining}%
\author[2]{Kaining Zhou}%
\author[3]{Adam Soffer}%
\author[2]{Jhonathan E. Ephrath}%
\author[3]{Ofer Hadar}%
\author[2]{Naftali Lazarovitch}%
\affil[1]{Affiliation not available}%
\affil[2]{French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel}%
\affil[3]{School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel}%


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  \date{May 25, 2024}


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\begin{abstract}
Minirhizotron technique (MR), an image-based root phenotyping
technology, has expanded our understanding of in situ root responses to
changing environmental conditions. However, the conventional manual
approaches to capturing and analyzing images are time-consuming, thus
constraining the size and frequency of sampling and interpretation. To
address this bottleneck, an automated MR system was developed, which
worked in a fully automatic manner according to the pre-set schedule.
Users can remotely control the system and download images. This system
was tested in a net house by imaging bell pepper roots on a daily basis,
which shows superior performance over commercial manual MR systems in
terms of image resolution and sampling frequency. Besides, an image
analysis model was built on convolutional neural networks to estimate
root length from MR images directly without segmentation in the training
process. This model was trained on a dataset of \textasciitilde{}18,000
tomato root images taken by a manual MR camera and was used for
estimating root length on 832 bell pepper root images taken by the
automated MR camera. The high correlation coefficient (R
\textsuperscript{2} = 0.854) between the model estimation and manual
measurement proved that this model generalizes well over different crop
roots and camera types. Therefore, high-frequency in situ root
phenotyping can be achieved by the automated Minirhizotron image
acquisition and analysis tools proposed here.%
\end{abstract}%



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