The overall goal of this procedure is to obtain comprehensive information on plant roots growing in soil-filled rhizoboxes by combining different imaging methods. This method can help to answer key questions in the field of plant phenotypying and breeding, such as the contribution of different root architectures, to better abiotic stress resistance. The main advantage of this approach is that it combines RGB imaging for root architecture and hyperspectral imaging for root functionality.
The potential of using hyperspectral imaging for roots extends towards a wide range of rhizosphere parameters because the spectral information can reveal small-scale physiochemical changes introduced by plant roots. Visual demonstration of this method is critical as the rhizobox filling steps have to be done carefully. Otherwise, root growth and root visibility will be negatively affected.
Begin this procedure with preparation of the rhizoboxes for substrate filling as detailed in the text protocol. Pre-wet the dry soil to a gravimetric water content of 0.108 gram per gram by adding 400 grams of water for 3, 705 grams of dry soil. Mix the soil and water gently to obtain a homogeneous water distribution.
Manually disrupt larger aggregates to keep the particle size smaller than or equal to two millimeters. It is critical to obtain a homogeneous layer of soil next the gas observation window avoiding air gaps. Root tips of of plants quickly dehydrate, growing into air gaps.
Also spectral image quality for water mapping is negatively affected. Fill the pre-wetted soil into the open rhizoboxes and compact it gently using a polystyrene sheet to cover the inner volume of the box, thereby resulting in a homogeneous bulk density of 1.3 grams per cubic centimeter. Add the remaining amount of water to achieve the target water content of 0.31 cubic centimeters per cubic centimeter by spraying onto the surface with a spray bottle.
Ensure a small drop size to avoid surface structure degradation, as well as homogeneous wetting. Keep the box on a balance during the spraying to monitor the amount of water actually added to the 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 17, 818 plus or minus 68 grams. Equip the climate room with eight LED lamps providing homogeneous illumination of 450 micromoles per square meter per second with spectral peaks at 440 and 660 nanometers for optimum plant growth. After setting the ambient parameters according to the plant and experimental needs, cover the glass window by a wooden plate to keep the root zone in the dark and to avoid algae growth due to light penetrating through the glass surface.
Then put the rhizoboxes at an inclination of 45 degrees using an adequate metal framework. This maximizes root growth towards the glass surface due to gravitropism. For RGB root imaging, illuminate the rhizobox using four 24-watt fluorescent light tubes attached at a distance of 80 centimeters from the rhizobox.
Also mount four 15-watt UV tubes at 20 centimeters from the rhizobox's alternative illumination making use of root autofluorescence in case of low contrast between root and bright-colored substrate background. Turn on the UV lamps and then mount the rhizobox to be imaged into the holder of the imaging box. Next, take two images to cover the upper and lower half of a rhizobox with an overlap of about three centimeters.
Acquire and process the RBG images as detailed in the text protocol. Finally run the analysis of the acquired RBG root images and subsequently control whether there are regions that 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 or increase the debris and rough edges filtering options. Perform image acquisition by first determining the camera integration times for the rhizobox scan and the white standard in the camera software.
To do so, 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 in a way that approximately 85%of the full dynamic range of the camera is used on the histogram displayed by the software. Setting the integration time correctly for different substrates and root tissues is critical to make full use of the dynamic range of the infrared camera by avoiding any loss of information by exceeding its range.
Repeat the process for the white standard by moving the camera positioning system to target the white standard before closing the camera software. Next open the Matlab imaging GUI and input all settings from the current rhizobox scan. Acquire the dark and white standards before each imaging run 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. For spectral-based measurement of water content, a calibration rhizobox is needed.
Subdivide a rhizobox into five centimeter compartments using polystyrene sheets to fill them with soil at different water contents. Scan the calibration rhizobox with the same settings as used for the planted rhizoboxes. As an example, for combining root and aboveground traits, obtain the leaf porometer for measurement of stomata conductance.
Equilibrate the device to ambient conditions for at least one hour in the climate chamber. Take measurements from at least three leaves per plant. A representative root growth image of sugar beet cultivar, Ferrara, based on RGB imaging is shown here.
At 35 days after sowing, the plant roots have elongated to reach the bottom of the rhizobox. Some root axes at the top of the rhizobox could not be segmented from RGB images. Senescence of the older basal roots changes their color into brown.
Therefore separation between these roots and the soil background based on a color threshold fails. Using hyperspectral imaging, segmentation is based on different spectral features of root foreground and soil background. This improves the segmentation result.
Differences in measured root length to a manually-tracked reference image are only 1.5%Furthermore, spectral imaging allows fine mapping of the water content around the root to infer on water uptake. Here are the root skeleton is shown in black. The lighter areas show regions of higher water depletion near to the root axes.
While darker blue areas represent regions with higher soil water content outside the reach of roots. After watching this video, you should have a good understanding how to properly fill rhizoboxes in order to ensure satisfactory root growth and visibility. This is the basis for later imaging and representative root phenotyping results.
Following this procedure, other methods for spectral classification like K-means clustering or support vector machines can be performed to obtain in-depth information about root and rhizosphere properties like root senescence and decomposition. Once established, this technique allows you to comprehensively phenotype root systems and abiotic stress response. A set of 10 cultivars can be characterized with a total experimental duration of less than three months.
