Name: RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols (Video) | JoVE Journal
Uploaded: 2017-08-08T15:00:00.000+00:00
Duration: 11 min 37 s
Description: Learn RGB and hyperspectral imaging protocols for plant root phenotyping in soil-filled rhizoboxes. This method combines architectural analysis with functional insights to advance plant breeding research.

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The authors acknowledge funding from the Austrian Science Fund FWF via the Project Number P 25190-B16 (The roots of drought resistance). Establishment of the hyperspectral imaging infrastructure was supported financially by the Federal Government of Lower Austria (Land Nieder&#246;sterreich) via the project K3-F-282/001-2012. Additional funding for the sugar beet experiment was received from AGRANA Research &#38; Innovation Center GmbH (ARIC). The authors thank Craig Jackson for technical support during the experiment and English correction of the manuscript. We also acknowledge Markus Freudhofmaier who contributed to establishment the RGB imaging setup and Josef Schodl for construction of the rhizobox mounting.

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The authors have nothing to disclose.

The protocols provide two complementary approaches for soil grown root system imaging. A critical step for reliable experimental results is filling of the rhizoboxes that has to ensure an even and homogeneous substrate layer at the front glass to provide tight root-soil contact at the observation window and avoid air gaps. This is the main reason to use comparatively fine sieved soil of &#60; 2 mm: Larger aggregates result in higher surface morphology at the observation window with voids between aggregates. Besides a higher risk of root tip dehydration, this also requires more complex image processing techniques for water mapping31.
Modifications of the protocol therefore focus on improved and quick filling of rhizoboxes. Currently filling time is about 30 minutes per box. Furthermore use of rhizoboxes with two glass windows for imaging from both sides and modifications to optimize illumination homogeneity for better RGB images are tested. Further hardware extension might also consider integration of planar optodes32 as well as capacitance imaging33 into the rhizobox system. This however is beyond current upgrading activities.
Software modifications focus on automatic image registration to fuse the top and bottom RBG images34. For hyperspectral imaging advanced unsupervised feature extraction approaches28 as well as more sensitive supervised target detection methods such as SVMs35 are tested. Thereby the hyperspectral data potentially allow for the assessment of multiple soil, rhizosphere and root properties36. Furthermore it is intended to develop a (semi)automatized software for rhizobox root images based on a modified version of Root System Analyzer37 to quantify morphological (length, diameter, surface) as well as architectural traits (branching frequency, branching angles).
The main limitation of the protocol compared to 3D imaging approaches is the restriction to the surface visible root and rhizosphere properties. However it has been demonstrated that the visible root traits are a reliable proxy for the whole root system21. The rhizobox technique is easily combined with traditional destructive sampling (washing) at the end of dynamic growth imaging in order to validate the relation of visible vs. total root system traits. As this relation might vary among species21, destructive sampling is recommended to ensure reliable inference from visible traits for any new phenotyping series with a different crop species.
The key advantage of the protocol presented here is the combination of realistic growing conditions (soil), relatively high potential throughput for the temporally resolved RGB imaging and inference on root functionality (e.g. water uptake) via the chemometric root and rhizosphere data from hyperspectral imaging. Thereby the methods overcomes inference restrictions in high throughput seedling and non-soil root imaging methods14, while it partially allows deep phenotyping insights into functional processes with less experimental complexity and higher throughput compared to advanced 3D methods15.
In upcoming experiments the protocol will be used to study the effect of mycorrhiza on root system development and functionality of legumes as well as for phenotyping root characteristics of cover crop species in relation to soil structure, nitrogen and carbon cycling.

Roots provide several essential functions for plants such as storage of assimilates, anchorage of terrestrial plants in soil, and uptake and transport of water and nutrients1. From an evolutionary point of view, the formation of root axes is considered a fundamental precondition for the origin of land plants2. In spite of this important role, historically roots have occupied only a marginal position in biological research. In more recent times, however, there is increasing scientific interest in plant root systems as evidenced in Figure 1.
<img alt="Root studies trend chart, exponential increase in plant study focus, decades 1960-present." class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg" data-altupdatedat="1788250015000" data-alt="Figure 1"> 
Figure 1: Relevance of root studies in plant sciences. 
Number of root related studies as a percentage of all published plant studies in SCI journals over the last decades. Search result from Scopus using keywords "plant" and "plant AND root". <a href="//cloudflare.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Two main reasons can be hypothesized to underlie the recent advances in root research. First, terrestrial vegetation is exposed to more frequent environmental stresses as a result of global change3. In the context of agricultural crop production it is estimated that globally around 30% of the agricultural area are limited by water and phosphorus4,5. Stress reduction of crop yields are a main reason for significant yield gaps that are globally estimated at lower 50% of potential productivity for rainfed agro-ecosystems6. Besides low resource availability, this is also related to poor resource use efficiency, i.e. insufficient capacity of a plant to exploit available resources7. This results in losses of mobile resources such as nitrate which can negatively affect other ecosystems. The current global nitrogen use efficiency for example is estimated at 47%8. Better resource use efficiency via improved management methods and cultivars is therefore of high importance for both sustained growth of agricultural outputs as well as for environmental sustainability. In this context plant roots are considered to be a key target for improved crops and cropping systems9,10.
A second important background for the recent interest in plant roots is technological advance in measurement methods. Root methods have long been restricted by two key challenges: for measurement of roots from plants growing in soil they had to be isolated for quantification, mostly by washing11, thereby disturbing the architectural arrangement of root axes. In-situ root observation using excavation methods, thereby conserving the natural location of roots in soil, have been used for botanical description12. Still they are very time-consuming and thus do not meet the throughput requirements of comparative structural-functional root system analysis. On the other hand high-throughput methods for root architecture measurement were mostly done on artificial media and for seedling plants13 where the extrapolation to the natural growth environment of plants is questionable14.
The recent boom of root research is tightly linked to the advance in imaging methods15. Imaging approaches in root studies can be roughly grouped into three types. First there are high resolution 3D methods such as CT and MRI16. These methods are most suitable to study interaction processes of plant roots with soil, such as drought induced xylem embolism17. Typically they are applied to comparatively small samples where they allow detailed observations. A comparison of CT and MRI for differently sized pots and fine root imaging is provided in18. Second, there are high-throughput imaging methods19,20. These methods are mostly based on common 2D RGB imaging of roots growing on artificial media (gel, germination paper) where high contrast allows comparatively simple dissection between roots and background. They are appropriate for high throughput comparison among seedling root traits of different crop genotypes under standardized artificial growing conditions13. In between these two approaches are rhizobox methods: they use 2D imaging of roots growing in soil over longer time period and have medium throughput21,22. A recent challenge to (2D) root imaging is to capture also indicators of root functionality in addition to description of structure23.
In the present paper we present the experimental protocols for imaging rhizobox grown root systems using (i) a cheap and simple custom-made RGB imaging setup and (ii) a more complex NIR imaging setup. Example results obtained from these two setups are shown and discussed in the context of plant phenotyping and plant physiological research.

<table><tbody><tr><td>Rhizobox</td><td>Technisches B&amp;uuml;ro f&amp;uuml;r Bodenkultur</td><td>Experimental builder</td><td></td></tr><tr><td>LED Lamps ATUM HORTI 600</td><td>Klutronic</td><td>AHI10600F</td><td></td></tr><tr><td>Fluorescent light tube HiLite T5 Day</td><td>Juwel Aquarium</td><td>86324</td><td></td></tr><tr><td>UV light tube Eurolite 45cm slim 15 W</td><td>Conrad</td><td>593384 - 62</td><td></td></tr><tr><td>Canon EOS 6D</td><td>Canon Austria GmbH</td><td>8035B024</td><td></td></tr><tr><td>Adobe Photoshop CS5 Extended Version 12.0 x 32</td><td>Adobe Systems Software Ireland Ltd.</td><td></td><td></td></tr><tr><td>WinRhizo Pro v. 2013</td><td>Regent Instruments Inc.</td><td></td><td></td></tr><tr><td>Xeva-1.7-320 SWIR camera</td><td>Xenics</td><td>XEN-000105</td><td></td></tr><tr><td>Spectrograph Imspector N25E</td><td>Specim</td><td></td><td></td></tr><tr><td>Hyperspectral imaging scanner</td><td>Carinthian Tech Research AG</td><td>Experimental builder</td><td>Design and assemblage of Hyperspectral Imaging Scanner and software</td></tr><tr><td>Matlab R2106a</td><td>Mathworks</td><td></td><td>Including Toolboxes for Image Processing, Signal Processing and Statistics and Machine Learning</td></tr><tr><td>AP4 Poromoeter</td><td>Delta-T-Devices</td><td></td><td></td></tr><tr><td>LI-3100C Area Meter</td><td>LI-COR</td><td></td><td></td></tr><tr><td>BASF Styradur polystyrene sheets</td><td>Obi Baumarkt</td><td>9706318</td><td>Different types of polystyrene sheets or other material separating differently moistured soil can be used</td></tr></tbody></table>

rgb and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

Example results are presented for root segmentation based on RGB and HS imaging. For spectral imaging an example of high resolution water mapping is provided. Finally results are shown that demonstrate the scientific context where image based root data are applied.
RGB based root measurement
Figure 8 shows an RGB root image time series of sugar beet cultivar Ferrara. The images reveal some artefacts from inhomogeneous illumination of the rhizoboxes, with brighter areas along the left side and different brightness at the overlapping area between top and bottom images.
<img alt="RGB root growth study, sugar beet 'Ferrara,' days 20-35, time-lapse analysis, growth diagram." class="xfigimg" src="/files/ftp_upload/56251/56251fig8.jpg" data-altupdatedat="1788250015000" data-alt="Figure 8"> 
Figure 8: Root growth time series from the RGB imaging. 
Pictures show the sugar beet cultivar Ferrara at different days after sowing (DAS). The images show some artefacts due to non-homogeneous illumination at the left side of the image and between top and bottom images. Scale bars, 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 9 provides details on root segmentation based on color thresholding for cultivar Ferrara at day after sowing (DAS) 35. As a reference (Figure 9A), a binary image is used where all roots were manually tracked with a Graphic Tablet. The time required for manual tracking of the entire, fully developed, dense sugar beet root system was around four hours. Figure 9B gives a detailed view on a selected area at the top of the image where old lateral roots are present. Here several root axes are not classified by the color threshold. At the bottom (Figure 9C) on the contrary, where white young roots are predominant, the color based segmentation properly classifies all root axes. The binarized root system (Figure 9D) shows a black area at the left side from the illumination artefact which was defined as exclusion region before running quantitative analysis. Figure 9E shows the corresponding pixel histograms of selected features (roots vs. soil) for the red channel of the RGB image from Ferrara at DAS 35. The root pixels (blue color) clearly show three peaks corresponding to bright young laterals, dark old laterals and tap root. The overlap between the old laterals and the soil background is very strong, leading to unclassified root axes (cf. Figure 9B).
<img alt="RGB color based root segmentation diagram with histogram analysis of root types and soil differentiation." class="xfigimg" src="/files/ftp_upload/56251/56251fig9.jpg" data-altupdatedat="1788250015000" data-alt="Figure 9"> 
Figure 9: Root segmentation using a color threshold. 
(A) Manually segmented root system using a Graphic Tablet, (B) area with poorly segmented old root axes in the top and (C) properly segmented young axes in the bottom of the image, and (D) binary image obtained from color based thresholding. (E) Pixel histograms for selected features of the RGB image. Roots are represented by the blue bars with different root types indicated; soil is represented by the red bars. Scale bars in A and D, 2 cm; scale bars in B and C, 1 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The resulting total visible root length quantified for the manually segmented reference image is 1534.1 cm, while the automatized, color based segmentation gives a total root length of 1427.6 cm.
Greyscale images from UV-illumination do not provide an advantage in the case shown here and performed worse compared to color thresholding (root length: 1679.7 cm). Old roots could not be segmented, and there was more noise in the image, probably due to lower light intensity of the UV lamps. However, in case of young roots with high auto-fluorescence and a bright background substrate, UV-illumination can still be an option as shown by an image obtained from another experiment where sand was used as background substrate (Figure 10).
<img alt="Fluorescence microscopy; root structure; left: blue-hued confocal, right: brightfield image." class="xfigimg" src="/files/ftp_upload/56251/56251fig10.jpg" data-altupdatedat="1788250015000" data-alt="Figure 10"> 
Figure 10: UV illumination to visualize roots on bright background. 
Example from a durum wheat root system growing in a rhizobox filled with quartz sand. The rhizobox is imaged with illumination for (A) UV light and (B) fluorescent (day) light. Scale bars, 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
HSI based root measurements
Figure 11 provides the mean spectra for three root ROIs (old and young lateral, tap root) and two soil ROIs (top and bottom of rhizobox).
<img alt="Root spectral analysis graph, normalized reflectance vs wavelength, soil and root comparisons." class="xfigimg" src="/files/ftp_upload/56251/56251fig11.jpg" data-altupdatedat="1788250015000" data-alt="Figure 11"> 
Figure 11: Mean spectra of root and soil. 
Spectra from regions of interest (ROIs) on the root (three root types) and in the soil (top and bottom of the rhizobox). The ROIs are selected to determine an optimum segmentation criterion between root and soil. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
It is evident that the tap and young lateral roots differ substantially from the background in intensity of most spectral bands. For the old laterals the intensity differences are much lower. A feature that can be inferred visually is the different slope of the spectrum around water absorption region (1450 nm). Here the slope of root spectra is higher compared to soil spectra. Furthermore a change of tap and young lateral spectra in the region around 1100 nm can be identified that does not occur in the old laterals.
Figure 12A shows the result from the search algorithm identifying a spectral ratio with strongest foreground-background contrast. The ratio of spectra at 1476 nm to 1076 nm provides the best separation between roots and soil. The resulting histogram of root foreground and soil background pixels is shown in Figure 12B. Although there is some overlap, most pixels are clearly separated from the soil background. Fitting a bimodal Gaussian curve through the histogram and using Bhattacharyya distance for quantification, a value of 7.80 is obtained. A value higher 3.0 indicates strong image contrast allowing reliable separation28.
<img alt="Spectral heatmap and histogram for wavelength intensity analysis; optical emission data analysis." class="xfigimg" src="/files/ftp_upload/56251/56251fig12.jpg" data-altupdatedat="1788250015000" data-alt="Figure 12"> 
Figure 12: Difference in reflectance between root foreground and soil background for different spectral band ratios and pixel histogram at spectral ratio used for segmentation. 
(A) Bright colors (yellow) show high contrast between foreground and background, dark colors (blue) show low contrast. The first 15 bands have been removed because of noise. The red lines indicate the band ratio with highest contrast. (B) Pixel histogram of roots (blue) and soil (red) at segmentation spectral ratio. Blue bars represent the root and red bars the soil. The intensity value corresponds to the ratio of spectral band 160 to spectral band 49. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The binary image (Figure 13) is created by applying a global intensity threshold of the identified spectral ratio at a value of 1.008 calculated from the histogram distance27. Analysis of root length of this image gives a total length of 1557.3 cm which represents an error of only 1.5% compared to the manually tracked reference image.
<img alt="Neural network structure diagram; dendritic tree; microscopy image; neuron connectivity visualization." class="xfigimg" src="/files/ftp_upload/56251/56251fig13.jpg" data-altupdatedat="1788250015000" data-alt="Figure 13"> 
Figure 13: Binary image of the root system of sugar beet cultivar Ferrara. 
The image is obtained by applying a global spectral threshold. Scale bar (bottom left corner), 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Although root segmentation has improved using spectral information compared to color based information, the main intention of HS imaging is analysis of chemometric image properties. This is exemplified via mapping the water content of a rhizobox image.
Figure 14A shows the mean spectra of the compartments in the calibration rhizobox (cf. Figure 7) filled with soil of different water content. The shape of spectra is similar between the compartments, i.e. here a spectral ratio does not necessarily provide a more stable classification criterion. Thus intensity at a single spectral band (1680 nm), where the average difference between adjacent water contents is maximized, is identified as best separating criterion. The resulting pixel histograms for this spectral wavelength are shown in Figure 14B.
<img alt="Spectral analysis graphs; (A) absorption spectra, (B) intensity distribution for wet-dry states." class="xfigimg" src="/files/ftp_upload/56251/56251fig14.jpg" data-altupdatedat="1788250015000" data-alt="Figure 14"> 
Figure 14: Spectral features for water content calibration. 
(A) Mean spectra of nine water compartments from the calibration rhizobox with different water contents; (B) Pixel histograms for the water compartments at band 216 where average distance between neighboring compartment is maximum. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The relation of the average pixel intensity at 1680 nm and the measured water content is shown in Figure 15.
<img alt="Soil moisture graph; exponential, linear fit. Water content vs reflectance; field capacity, wilting point." class="xfigimg" src="/files/ftp_upload/56251/56251fig15.jpg" data-altupdatedat="1788250015000" data-alt="Figure 15"> 
Figure 15: Relation of spectral reflectance and volumetric water content. 
The figure shows data pairs of measured water content and spectral reflectance with empirical curves (linear and exponential) fit to the data excluding the highest water contents (red triangles). <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig15large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Differentiation of higher water contents from spectral intensity becomes difficult. A significant regression (either linear or exponential) with high R2 can be fit to water contents up to around 0.30 cm3 cm-3. Wetter soil conditions cannot be reliably predicted by the intensity value. Similar behavior of an exponential relation between reflectivity and water content with a decreasing response to water contents higher 0.30 cm3 cm-3 was also found in other studies30.
A rhizobox image with fine mapping of water content is shown in Figure 16. Four aspects have to be remarked. First, a region of lower water content can be seen in the rooted parts of the rhizobox. Second, strongest depletion is concentrated in the vicinity to single root axes. Third, depletion zones also occur where no root axes are visible on the surface, indicating regions where roots are hidden in soil. Fourth, water mapping without further image-processing results in a patchy appearance due to the aggregated soil. This can indicate inhomogeneous water content distribution at the aggregate scale, but also surface morphology effect on image quality. Chemometric image-processing techniques are an option to overcome such morphological effects in spectral images31, but are not implemented so far in the Matlab scripts used here.
<img alt="Neuron structure diagram; detail of dendritic branches in neural network analysis." class="xfigimg" src="/files/ftp_upload/56251/56251fig16.jpg" data-altupdatedat="1788250015000" data-alt="Figure 16"> 
Figure 16: Water content mapping on a rhizobox. 
The dark blue colours represent regions of high water content, green to red areas show regions with low water content. The plant root is overlaid on the image in black. Scale bar (bottom left corner), 2 cm. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig16large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Application examples
Figure 17 relates quantitative root traits from image analysis with aboveground measurements.
<img alt="Plant growth data analysis; graphs of root length, surface area ratio, dry matter, stomata conductance." class="xfigimg" src="/files/ftp_upload/56251/56251fig17.jpg" data-altupdatedat="1788250015000" data-alt="Figure 17"> 
Figure 17: Typical application examples for root data. 
(A) and (B) show root information used for aboveground-belowground plant characterization in a phenotyping context. (A) represents root growth from the sugar beet cultivar Ferrara, (B) compares six rhizobox grown sugar beet cultivars using leaf-to-root area ratio (data from one replicate). (C) and (D) are functional relations between traits as found in plant physiological research. (C) shows the influence of leaf-to-root area ratio on dry matter production and (D) the relation of root surface area to stomata conductance. <a href="https://cloudflare.jove.com/files/ftp_upload/56251/56251fig17large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 17A and 17B are relevant for phenotyping focusing on comprehensive aboveground and belowground plant characterization. Figure 17A shows root growth of sugar beet cultivar Ferrara (cf. Figure 8 for images). Expansion of the root system indicates the capacity of a cultivar to explore the soil volume in a given time span of the vegetation period. Figure 17B shows leaf-to-root surface area ratio of six sugar beet cultivars, providing a descriptor for the balance between plant supply (root) and demand (leaf).
Figures 17C and 17D give examples for functional relations of interest in physiological research. In Figure 17C leaf-to-root surface area ratio is related to dry matter formed during the experiment, indicating the predominant role of the assimilating surface as a limiting factor for dry matter accumulation. The lack of significance in spite of a comparatively high R2 is related to the low number of paired data (n=6) used here. Figure 17D reveals that cultivars with higher root surface area (improved uptake) have an average higher stomata conductance over the course of the experiment. The higher root area apparently sustains water extraction, thereby prolonging stomata opening.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

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.

rgb-spectral-root-imaging-for-plant-phenotyping-physiological

Research

JoVE Journal

Environment

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Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

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Analysis of Arabidopsis thaliana Growth Behavior in Different Light Qualities

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An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

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Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

We use simple laboratory tools to examine the root system architecture (RSA) of Arabidopsis and Medicago. The plantlets are grown hydroponically over mesh and spread using an art brush to reveal the RSA. Images are taken using scanning or a high-resolution camera, then analyzed with ImageJ to map traits.

A Simple Protocol for Mapping the Plant Root System Architecture Traits

We designed an image-based phenotyping protocol to determine the morphological and physiological responses to single and combined heat, drought, and waterlogging treatments. This approach enabled the identification of early, late, and recovery responses at a whole plant level, particularly above-ground parts, and highlighted the necessity of using multiple imaging sensors.

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry

Understanding the role of environmental heterogeneity in species coexistence has typically focused on types of heterogeneity that are extrinsic to the community’s species composition. We provide novel detailed methods for creating soil heterogeneity treatments using soils subject to plant-soil feedback conditioning, or heterogeneity intrinsic to the community composition.

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

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Protocols for Robust Herbicide Resistance Testing in Different Weed Species

Learn RGB and hyperspectral imaging protocols for plant root phenotyping in soil-filled rhizoboxes. This method combines architectural analysis with functional insights to advance plant breeding research.

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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