1. Rhizoboxes for plant growth NOTE: The experimental system uses rhizoboxes to grow plants for root imaging. First the design of the boxes and the substrate used are described, and then details on the filling procedure are given. Design of rhizoboxes Create rhizoboxes (Figure 2) with a back plate and side frames made of grey PVC with 15 mm strength. Use a box size of 300 mm x 1000 mm. For the front window, use 6 mm mineral glass which is attached to the PVC frame by metal rails being screwed into the side walls. Create three holes on the bottom frame to allow drainage of excess water. These holes can be optionally closed by plastic screws. Before filling, adapt the inner diameter of the rhizobox (between 10 mm and 30 mm) by inserting PC multiwall sheets. An inner space of 10 mm is recommended for most crops to reduce the weight (rhizobox weight without soil is 13.2 kg) of the entire system. Figure 2: Rhizobox experimental system and its components. Left figure shows the dimensions of a rhizobox and right figure its single components, being a grey PVC back plate with side frame, a front mineral glass, multiwall sheets for variable inner diameter and metal angles to fix the front glass to the back compartment. Please click here to view a larger version of this figure. Substrate Fill the rhizoboxes with field soil (for this experiment: silt loam top soil from a calcareous chernozem) sieved to 2 mm particle size. Open the rhizoboxes for filling the substrate into the inner compartment (back plate with side frames) in horizontal position using pre-wetted substrate. Fill horizontally to avoid layering and segregation between fine and coarse particles which occurs when filling in vertical position by pouring the substrate through the upper opening. Pre-wet the substrate before filling. Depending on the type of substrate (particularly on its silt and clay content), do not exceed a water content of 0.12-0.18 cm3*cm-3 to avoid smearing and structure degradation. Add the difference between pre-mixing and target water content after filling the substrate into the rhizoboxes. NOTE: Filling with (oven-)dry substrate and subsequent addition of the entire water is not recommended as is can result in strong settlement of the substrate and formation of large cracks. Step by step filling example Define the target water content. Here it is initially set at 80% plant available water (PAW) where plants do not suffer from any water shortage. Determine field capacity (FC) and permanent wilting point (PWP) of the substrate. Here obtain FC using a PVC tube of equivalent height (100 cm) as the rhizoboxes. Close the tube at the bottom with a stubble having small drainage holes, add 1 cm of gravel to avoid closure of the holes from finer substrate and fill the substrate to the same bulk density as used for the rhizoboxes (1.3 g cm-3). Saturate the tube with water until drainage occurs and leave it for two days to equilibrate (the resulting water content is per definition equivalent to field capacity), while covering the upper opening with cling-film to avoid evaporation. The field capacity value achieved for the soil in this experiment was 0.357 cm3 cm-3. NOTE: Water content at PWP has to be known in advance from standard soil physical methods (e.g. pressure plate measurements24) or from texture based pedotranfer functions25. Here it is equal 0.12 cm3 cm-3 for the soil used. When measuring FC by pressure plate extraction too, take the water content at a matrix potential of h=-100 hPa and not h=-330 hPa in order to correspond to the rhizobox geometry (height of 100 cm = 100 hPa). Calculate the water content (WC) at 80% PAW: WC (cm3 cm-3) = 0.80 (FC-PWP) + PWP. For the hydraulic limits of the soil used here this gives a volumetric water content at 80% PAW of 0.31 cm3 cm-3. Calculate the water amount for a rhizobox volume of 2850 cm3 (30 cm width, 1 cm inner space, 95 cm in height, with top 5 cm keeping free of substrate for watering). This gives a water volume of 883.5 cm3 equal 883.5 g for the density of water being 1.0 g cm-3 at 20 °C. Define the bulk density (db) to fill the rhizoboxes. Here, set at 1.3 g cm-3 corresponding to values typically found in agricultural field soils. The amount of dry substrate required to fill a rhizobox volume of 2850 cm3 at this db equals 3705 g of dry soil. Pre-wet the dry soil to a gravimetric water content of 0.108 g g-1 (equal to a volumetric water content of 0.14 cm3 cm-3) by adding 400 g of water for 3705 g of dry soil and mix it gently to obtain a homogeneous water distribution. Manually disrupt larger aggregates to keep the particle size ≤ 2 mm. Fill the pre-wetted soil into the opened rhizoboxes and compact it gently using a polystyrene sheet (30 x 10 x 1.5 cm) to cover the inner volume of the box, thereby resulting in a homogeneous db of 1.3 g cm-3. Add the remaining amount of water (483.2 g) to achieve the target water content of 0.31 cm3 cm-3 by spraying onto the surface with a spray bottle. Ensure small drop size to avoid surface structure degradation and homogeneous wetting. Keep the box on a balance during spraying to monitor the amount of water actually added to substrate. Let the water redistribute for 10 minutes and then press the glass onto the surface and fix it with the side metal rails. The average final weight of rhizoboxes with wetted substrate was 17818 ± 68 g (13230 g rhizobox weight + 3705 g dry soil + 883 g water). NOTE: The homogeneous water content filled in the horizontal boxes will redistribute when the boxes are set in their final position according to the resulting potential gradient. This is a physical process in all plant growth pots according to their geometry (height) and experimenters should be conscious on their pot hydraulics26. 2. Climate room setup Equip the climate room (Figure 3) with 8 LED lamps providing homogeneous illumination of 450 μmol m-2 s-1 with spectral peaks at 440 (blue) and 660 (red) nm for optimum plant growth. Figure 3: Climate chamber with rhizoboxes for stress experiment. (A) Left view of entire chamber with LED illumination, weather station and PC (here for logging leaf hygrometers); right view with a close-up of the metal frame holding rhizoboxes in 45 ° inclination and wooden plates used to shied rhizobox glass window against light. (B) Stress experiment with sugar beet combining four stages with stress due to different atmospheric demand (high/low) and soil water availability (high/low). Green bars of mean stomata conductance give an indication of plant stress response. Please click here to view a larger version of this figure. Set the ambient parameters according to plant/experimental needs. Here, use 14 hours light and 10 hours dark for illumination. During plant establishment and before stress treatments started, set the temperature to 20° C during day and 15° C during night and keep relative humidity at 50 ± 8%. Put the rhizoboxes at an inclination of 45° using an adequate metal framework. This maximizes root growth towards the glass surface due to gravitropism. Cover the glass window by a wooden plate to keep the root zone in dark and avoid algae growth due to light penetrating through the glass surface. 3. Sugar beet example setup and treatments Pre-germinate sugar beet seeds on wet filter paper for three days at 20 °C in an incubator until the radicle emerges. This ensures seeding with viable plants. NOTE: Pre-germination is not required for plants with high germination vigor, thereby avoiding the risk of damaging the radicle at seeding. For sugar beet seeds with a thick pericarp, however the risk of non-viable seeds is high and pre-germination significantly accelerates the emergence of the radicle compared to direct seeding into soil. Drill a small hole to about 1.5 cm soil depth in the middle of the rhizobox with a screwdriver, position one seed in it using tweezers with the radicle oriented downwards and next to the glass window (this improves the initial visibility) and gently cover it with soil. Add a 0.5 cm layer of fine gravel (2-4 mm) on top of soil to protect the soil aggregates from slaking during irrigation and reduce evaporation losses. To facilitate emergence, keep the soil surface free of gravel where the seed has been positioned. Add 10 g of water to enhance establishment. During establishment and early growth until experimental stress treatments start, irrigate the rhizoboxes every 2-4 days to keep the initial moisture content of 80% PAW. Determine the amount of irrigation water required by weighing the rhizoboxes and adding water until achieving the initial weight of each individual box. For manual irrigation use a pipette to avoid surface structure degradation during watering. Arrange the rhizoboxes according to the established design in the climate room. The protocol reported here is based on an experiment with six sugar beet cultivars in five replicates in a completely randomized design (CRD). Reposition rhizoboxes each time when taking them out of the metal holder for weighing and watering. This avoids any effects of residual (light) inhomogeneity inside the climate room. Define the time for onset of stress treatments and the type of stress. The following settings (Figure 4) are used here. Start measurements at BBCH 15 (five leaves unfolded) with roots covering around 75% of the rhizobox depth and the canopy being sufficiently developed for measurements at the leaves. Keep each stage for at least three days to ensure adaptation to the new settings and make measurement. For the non-stress observations keep the initial settings with optimum soil moisture (80% PAW) and ambient conditions (20° C/15° C temperature, 50 ± 8% rH) giving a daytime vapor pressure deficit (VPD) of 1.28 ± 0.1 kPa (cf. Figure 3 C). Raise the atmospheric demand to a daytime VPD of 2.45 ± 0.4 kPa by increasing the temperature to 27°C/20°C and decreasing rH to 35%, while keeping soil moisture at 80% PAW. Subsequently dry down the rhizoboxes to 40% PAW equivalent to a water content of 0.215 cm3 cm-3 by withholding irrigation. Reset the initial ambient conditions with low atmospheric demand (VPD of 1.28 kPa). Combine stresses increasing the atmospheric demand to a VPD of 2.45 kPa and keep soil moisture at 40% PAW. 4. Root imaging methods Combine imaging methods to make use of their respective advantages and depending on the information targeted. Apply RGB imaging in the VIS range to track root growth, architecture and morphology over time which implicitly requires frequent measurement. Advantages of RGB imaging are (i) low costs, (ii) rapid image acquisition, (iii) low requirements of hard-disk space (image size: 48 MB) and (iv) high resolution (3648 x 5472 pixels). Use hyperspectral imaging (HSI) in the NIR range when chemometric features of root and soil are required. Advantages are (i) spectral features for segmentation between roots and soil background and (ii) access to physico-chemical system properties (e.g. soil water content, root age). Disadvantages are (i) higher scanning time (about 16 minutes per rhizobox), (ii) large size of datasets (13.7 GB per rhizobox image) and (iii) lower resolution of the NIR camera (320 x 256 Pixel), and (iv) higher complexity of data analysis. RGB root imaging Use an imaging box (Figure 4) that shields from ambient light and fixes the camera position consisting of a metal frame with a width of 1 m and a height of 1 m with side walls lined with pressboards. At the front end, fix the camera at two positions at a distance of 80 cm from the rhizobox. Attach a tapeline on the rhizobox frame with transparent adhesive tape and position the rhizobox in the holder of the imaging box. Illuminate the rhizobox using four 24 W fluorescent light tubes attached at a distance of 80 cm from the rhizobox. Also, mount four 15 W UV tubes at 20 cm from the rhizobox as alternative illumination making use of root auto-fluorescence in case of low contrast between root and (bright colored) substrate background. Take two images (top and bottom position) to cover the upper and the lower half of a rhizobox with an overlap of about 3 cm. Acquire RGB images with a digital single-lens reflex camera which is fixed by quick release plates on the respective positions of the imaging box. Apply the following settings when using the fluorescent light tubes. Adapt these example settings for any changes in dimensions and illumination as well as the camera model. Turn off autofocus and stabilizer on the camera objective. Set camera to manual mode. Set ISO speed to 500. Set shutter speed to 13. Set Aperture to 5.6. Turn off the mirror lock. Set the white balance to Auto White Balance. Use the following settings for illumination with UV tubes: Turn off autofocus and stabilizer on the camera objective. Set camera in manual mode. Set ISO speed to 1000. Set shutter speed to 13. Set Aperture to 5.6. Turn off the mirror lock. Set the white balance to Fluorescence. Merge the RGB images from top and bottom of the rhizobox into a single image a photo editor (e.g., Adobe Photoshop). Use the tapeline at the side frame of the rhizoboxes and control overlapping objects (root and soil features). Copy the two separate images, each with pixels size of 3648 x 5472, into a new file of size 3648 x 10944 pixels and white background. Reduce the layer opacity for one image to 60% and align overlapping parts of the images (tapeline, objects). Thereafter restore layer opacity to 100%. Based on the ruler on the image add two red lines of exactly 1 cm to the top of the image where no roots are present. These lines are later used at image analysis to scale the image to correct length dimensions. Merge all layers and remove parts of the image outside the soil filled window with the cropping tool. Save the image as tiff-file for further analysis. In case of images with UV illumination, reduce colors to greyscale before saving using the toolbar Adjustments-Black & White, and selecting the predefined High Contrast Blue filter. For segmentation between roots and soil background as well as for quantification of root traits of interest (e.g. length, surface, diameter, branching) use any root analysis software (see www.plant-image-analysis.org for available tools). Here WinRhizo is used. Open the rhizobox tiff-image the software. Calibrate the length scale of the image using the scaling bars added to the image. Select Based on Color in the Analysis menu where selecting Root & Background Distinction. Define a calibration file with color classes corresponding to roots and soil (background). For the example images used here (cf. Figure 8) three root color classes (old laterals, young laterals, tap root) and three soil color classes are defined. For the greyscale images (UV-illumination) select (i) Based on grey levels, and (ii) Pale Root on Black Background in the Root & Background Distinction menu and use a local Lagarde intensity threshold for segmentation. Open a data file where results are saved. Run the analysis and subsequently control whether there are regions (e.g. at the edges) which are mismatched. In this case define an exclusion region and restart the analysis. For roots not classified, add additional color classes and restart the analysis. For elements wrongly classified as roots, activate/increase the Debris & Rough Edges filtering options. Figure 4: Imaging box to acquire RGB rhizoboxes pictures. Left view of front side where rhizoboxes are attached for imaging with light sources inside; right view of backside where the camera is mounted. Please click here to view a larger version of this figure. Hyperspectral root imaging Hardware setup Use a hyperspectral root imaging system (Figure 5) consisting of (i) a thermo-electrically cooled 14-bit monochrome NIR camera with a spectral range from 900 nm to 1700 nm, 320 by 256 pixels and a frame rate of 100Hz and (ii) an imaging spectrograph with a spectral range 900 nm to 2500 nm and a spectral resolution of 3.6 nm. Arrange a halogen line illumination source (four 50 W halogen spots) in a 45°/-45° geometry. Mount the imaging sensor on a two-axis positioning system. The scan window has a size of 240 x 1000 mm, i.e. 30 mm at each edge of the rhizobox are not covered by the image. Control the system by a Matlab script for (i) white and dark standard acquisition, (ii) setting of camera integration time, (iii) selecting spatial (pixel size 0.1 mm; pixel size 1.0 mm) and spectral resolution (all 222 spectral bands with a resolution of 3.6 nm; smoothed spectrum with 54 bands and a resolution of 14.8 nm), and (iv) defining the scan region on the rhizobox. Save images as SIF-files. To avoid problems during saving of large files, subdivide each scanned image stride (9 strides per rhizobox) into four segments (three of 300 mm length, one of 100 mm length) and save separately with a unique file name consisting of stride number (1 to 9) and part (1 to 4) as well as date and time (YYYY.MM.DD HH:MM:SS). An entire rhizobox scan requires a hard-disk space of 13.7 GB. Image acquisition and analysis. NOTE: Figure 6 shows the steps of image acquisition, segmentation and analysis. Image acquisition comprises selection of camera setting for optimum image quality and definition of scan parameters. Determine the camera integration times for the rhizobox scan and the white standard in the camera software. Open the imaging GUI and move the camera to a position of the rhizobox where roots are present. Adjust the integration time of the camera targeting a light object (i.e. root) in a way that approximately 85% of the full dynamic range of the camera is used on the histogram displayed by the software. Repeat for the white standard by moving the camera positioning system to target the white standard. Then close the camera software. Open the Matlab Imaging GUI and make all settings for the current rhizobox scan. For the data reported here, use the following settings: Integration time white standard: 1000 Integration time rhizobox: 4000 Spectral resolution: Full resolution (i.e. 222 narrow-range spectral bands) Full spatial resolution (pixel size of 0.1 mm) Acquire the dark and white standards before each imaging run, e.g. once a day. The dark standard represents the camera noise, while the white standard gives the maximum reflectivity. These data are required for image normalization during pre-processing. Define whether the entire rhizobox or only part of it is scanned. For the present case entire rhizoboxes are imaged. Then start the scan. Process the image with a Matlab script. Operations performed by the script are described. NOTE: Scripts are currently in an undocumented version and can be obtained from the corresponding author. After proper documentation, they will be available for download from the website of the corresponding author's institution (www.dnw.boku.ac.at/pb/). Compose an entire image stride from the rhizobox center (containing roots) merging the four parts of the stride. NOTE: At this stage it is neither necessary nor recommended to use a spectral image of an entire rhizobox (i.e. all 9 strides) as the file size will make each calculation step in Matlab very time consuming and the information contained in one central stride is sufficient for the first steps of image analysis. Normalize the image using the acquired dark and white standards and taking into account the different integration times of white standard and rhizobox scans which are saved automatically during scanning in a file. Optionally apply a smoothing filter to remove noise from the image. The script currently offers 3×3 kernel median filtering and multiple scatter correction. For the image evaluation presented here, no filters are applied. Display the image at all recorded spectral bands to obtain a first insight and decide a wavelength to be displaying for selecting regions of interest (cf. image segmentation). Perform segmentation between roots and soil background in a separate script with the following steps. Select regions of interest (ROI) for root and soil to find spectral features for segmentation. Use the freehand selection tool to mark a ROI on the image displayed at a wavelength previously identified with good contrast between roots and soil. Here, use three ROIs on the root (old and young laterals, tap root) and two ROIs in the soil (dry, wet region). Display a rectangle with the selected ROIs and the remaining part as a black mask and visualize the selected ROI image at all wavelengths. Remove all lines in the image matrix containing pixels of spectral intensity = 0 (black pixels). Fuse the root and soil ROIs into one foreground (root) and one background (soil) matrix for segmentation. Search spectral bands (intensity of single spectra or spectral ratios) providing the best separation between root foreground and soil background27. Quantify the distinction between the resulting pixel histograms for root and soil using Bhattacharyya distance28. Select a threshold intensity value separating the histograms. Create a binary image by applying the selected threshold to the original image. This sets all pixels having smaller intensity than the threshold to zero and those with higher intensity to one (done automatically be the script). Save the binary image as tiff-file. Open the binary image and analyze root traits. Select (i) Based on grey levels, and (ii) Pale Root on Black Background in the Root & Background Distinction menu and use global intensity threshold (the image is already binarized). For mapping the water content from the hyperspectral data acquire a calibration dataset and apply a calibration equation to a rhizobox image. Subdivide a rhizobox into 5 cm compartments using polystyrene sheets to fill them with soil (same substrate and db as used for the experiment) at different water contents (Figure 8). Calculate the respective amounts of water to be mixed with soil and fill the compartments (same procedure as described in 1.3. for the entire rhizobox). Scan the calibration rhizobox with the same settings as used for the planted rhizoboxes. Perform the following steps using a script. Merge the four parts of a stride from the water calibration box to one stride and normalize it with dark and white standards. Select rectangular boxes at each compartment with different water content and save them in a structure array. Determine the spectral feature that best separates the water content compartments. This is done with a search algorithm for the global maximum of the intensity differences between mean spectra of adjacent water contents. Calculate the mean spectral intensity value for this feature for each water content compartment of the calibration rhizobox. Fit a regression equation through the directly measured water content and the respective spectral quantifier. Apply the regression equation to each pixel not classified as root on a rhizobox image and for the spectral feature (band) determined above which best relates to water content. Figure 5: Hyperspectral root scanner. The main components of the scanner are indicated. The small picture shows the camera during imaging of a rhizobox. Please click here to view a larger version of this figure. Figure 6: Steps in hyperspectral root imaging. Hyperspectral root imaging consists of three mains steps being (i) image acquisition, (ii) image segmentation and (iii) analysis of the spectral data. Please click here to view a larger version of this figure. Figure 7: Rhizobox for water calibration. The rhizobox contains compartments with substrate at different water content which are subdivided by polystyrene sheets. Germination paper at the dry compartments ensures that soil particles do not rinse into neighboring compartments. Please click here to view a larger version of this figure. 5. Application examples NOTE: Quantitative root information is applied in the context of plant phenotyping (cultivar comparison) and for plant physiological research. The following aboveground data are reported to exemplify these cases. Leaf area: Measure leaf area non-destructively at selected stages during the experiment via the length and width of leaves as a proxy. Alternatively canopy images can be used29. At the end of the experiment measure leaf length and width together with area of clipped leaves using a leaf area meter. Calibrate the non-destructive method applying a regression equation to the data pairs. Dry matter: At the end of the experiment, measure aboveground dry matter by clipping the plants with scissors and dry them for 24 hours at 105 °C in an oven. Stomata conductance: Measure stomata conductance with a leaf porometer. Before measurement, keep the device for at least one hour in the climate chamber to allow sensors equilibrate with ambient conditions and calibrated the device each time when ambient settings in the climate chamber are changed. Take measurements from at least three leaves per plant.