Root image acquisition and determination of root growth rate
Starting two days after seedling transfer to rhizoboxes (30th July 2021), roots were pictured from the transparent window of rhizobox with a digital camera (Canon EOS 5D Mark III) through a 28-mm lens. Pictures were taken on alternate days starting from 2nd August 2021 until the roots reached the bottom of the rhizobox (18th August 2021) giving a total of 9 measurement times in 18 days. All the root pictures were cropped within ImageJ (Schindelin et al. 2012) to exclude rhizobox boundaries for further analysis. To detect all the roots of our images, we trained a convolutional neural network using RootPainter (Smith et al. 2022). For this purpose, we used RootPainter to generated a dataset of images by randomly selecting three sub-regions pe cropped image (width: 861 pixels, height: 897 pixels) and annotate the roots from this image dataset to improve the model until it was able to identify most of the roots in our images. After achieving satisfactory performance of the model, we used it to segment all of our original images and extract the roots present in them. Segmented pictures were used to determine the total visible root length (VRL) by using RhizoVision Explorer using the batch-processing mode (Seethepalliet al. 2021) (Fig. 1a). Visible root length was plotted against time and the slope of this regression was used as a proxy for root growth rate (RGR).
\begin{equation} \text{RGR\ }\left(slope;cm\ \text{day}^{-1}\right)=\frac{\text{Δ\ }Visible\ root\ length\ (cm)}{\text{Δ\ }Time\ (day)}\nonumber \\ \end{equation}
Afterward, plants were moved to the greenhouse facility of Leuphana University of Lüneburg on 19th August 2021 and allowed to acclimatize to the greenhouse conditions (comparable to climate chamber conditions: day/night temperature and relative humidity were 22/15.3 °C and 60/73%, respectively) for two days before installing the root exudation traps.