Name: rgb and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols
Uploaded: 2017-08-08T15:00:00
Duration: 11 min 37 s
Description: Watch this Scientific Journal Video about RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols at JoVE.com

De auteurs erkennen financiering uit de Oostenrijkse wetenschap Fonds FWF via het Project nummer P 25190-B16 (de wortels van de droogte resistentie). Oprichting van de Hyperspectrale beeldvorming infrastructuur werd financieel ondersteund door de federale regering van Neder-Oostenrijk (Niederösterreich Land) via het project K3-F-282/001-2012. Aanvullende financiering voor de suikerbieten experiment van AGRANA onderzoek werd ontvangen &#38; Innovation Center GmbH (ARIC). De auteurs bedanken Craig Jackson voor technische ondersteuning tijdens het experiment en de Engelse correctie van het manuscript. Wij erkennen ook Markus Freudhofmaier die hebben bijgedragen aan de vestiging de RGB-imaging setup en Josef Schodl voor bouw van de montage van de rhizobox.

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De auteurs hebben niets te onthullen.

De protocollen bieden twee complementaire benaderingen voor grond gekweekte wortelsysteem imaging. Een cruciale stap voor betrouwbare experimentele resultaten is het invullen van de rhizoboxes die moet zorgen voor een zelfs en homogene substraat laag op het front glas bieden strakke wortel-bodem contactpersoon bij het kijkvenster en voorkomen van de luchtspleet. Dit is de belangrijkste reden voor het gebruik relatief fijne gezeefde bodem van &#60; 2 mm: grotere aggregaten resulteren in hogere oppervlakte morfologie bij het kijkvenster met leegtes tussen aggregaten. Naast een hoger risico van wortel tip uitdroging vereist dit ook complexere afbeelding verwerkingstechnieken voor water toewijzing31.
Wijzigingen van het protocol is daarom gericht op verbeterde en snel vullen van rhizoboxes. Momenteel vullen tijd is ongeveer 30 minuten per doos. Bovendien gebruik van rhizoboxes met twee glazen ramen voor beeldvorming van de beide zijden en wijzigingen aan het optimaliseren van de homogeniteit van de verlichting voor betere RGB-afbeeldingen worden getest. Verdere uitbreiding van de hardware zou ook integratie van vlakke optodes32 , alsmede precisiecapaciteit imaging33 in het rhizobox-systeem kunnen overwegen. Dit valt echter buiten de huidige upgraden activiteiten.
Software wijzigingen gericht op automatische beeldregistratie fuse boven en beneden RBG beelden34. Hyperspectrale benadert beeldvorming geavanceerde functie van ongecontroleerde winning28 evenals gevoeliger gecontroleerde doel detectiemethoden zoals SVMs35 worden getest. Waardoor maakt de hyperspectrale gegevens mogelijk voor de beoordeling van meerdere bodem, rhizosfeer en wortel eigenschappen36. Bovendien is bedoeld voor het ontwikkelen van een (semi) geautomatiseerde software voor rhizobox wortel afbeeldingen op basis van een gewijzigde versie van het wortelsysteem Analyzer37 te kwantificeren morfologische (diameter, lengte, oppervlak) en architecturale kenmerken (vertakkende frequentie, vertakking van de hoeken).
De belangrijkste beperking van het protocol in vergelijking met 3D imaging benaderingen is de beperking tot de oppervlakte zichtbaar wortel en de eigenschappen van de rhizosfeer. Toch is gebleken dat de zichtbare wortel trekken een betrouwbare proxy voor de hele root systeem21 zijn. De rhizobox-techniek is gemakkelijk gecombineerd met traditionele destructieve bemonstering (wassen) aan het einde van de dynamische groei imaging om te valideren van de relatie van zichtbare vs. totale wortelsysteem eigenschappen. Zoals deze relatie tussen soorten21 variëren kan, wordt destructieve bemonstering aanbevolen om een betrouwbare afleiding van de zichtbare eigenschappen voor elke nieuwe fenotypering series met een ander gewas soorten.
Het belangrijkste voordeel van het hier gepresenteerde protocol is de combinatie van realistische groeiomstandigheden (bodem), relatief hoge potentiële doorvoer voor stoffelijk opgelost RGB beeldvorming en gevolgtrekking op basis functionaliteit (bijvoorbeeld de opname van water) via de chemometric wortel en rhizosfeer gegevens van Hyperspectrale beeldvorming. Waardoor de methoden overwint gevolgtrekking beperkingen in hoge-doorvoer zaailing en niet-bodem wortel imaging methoden14, terwijl hierdoor gedeeltelijk diepe fenotypering inzicht in functionele processen met minder experimentele complexiteit en hogere doorvoer in vergelijking met geavanceerde 3D methoden15.
In de aankomende experimenten het protocol worden gebruikt voor het bestuderen van het effect van mycorrhiza wortelsysteem ontwikkeling en functionaliteit van peulvruchten, ook wat betreft fenotypering wortel kenmerken in verband met de bodemstructuur, stikstof en koolstof soorten cover gewas fietsen.

Wortels voorzien planten zoals opslag van verschillende essentiële functies assimileert, ankerplaats van terrestrische planten uit de bodem, en de opname en het transport van water en voedingsstoffen1. Vanuit een evolutionair oogpunt, de vorming van wortel assen wordt beschouwd als een fundamentele voorwaarde voor het ontstaan van land planten2. Ondanks deze belangrijke rol, hebben historisch wortels bezet slechts een marginale positie in biologisch onderzoek. In recentere tijden, echter er is steeds meer wetenschappelijke belangstelling in plant wortelstelsel zoals blijkt uit Figuur 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
Figuur 1: Relevantie van wortel studies in Plantenwetenschappen. Aantal wortel gerelateerde studies als een percentage van alle gepubliceerde plant studies in SCI tijdschriften in de afgelopen decennia. Zoekresultaat van Scopus met behulp van de trefwoorden "plant" en "plant en root". <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Klik hier voor een grotere versie van dit cijfer.</a>
Twee belangrijke redenen kunnen worden veronderstelde om ten grondslag liggen aan de recente vooruitgang in het onderzoek van de wortel. Eerste, terrestrische vegetatie wordt blootgesteld aan vaker milieu benadrukt als gevolg van veranderingen in het aardsysteem3. In het kader van landbouwgewassen productie geschat wordt dat wereldwijd ongeveer 30% van het landbouwareaal wordt beperkt door water en fosfor4,5. Stress reductie van gewasopbrengst zijn een hoofdoorzaak van significante opbrengst leemtes die wereldwijd worden geschat op lagere 50% van de potentiële productiviteit voor neerslag agro-ecosystemen6. Naast lage resourcebeschikbaarheid, is dit ook gerelateerd aan arme gebruik efficiënt beheer van hulpbronnen, d.w.z. onvoldoende capaciteit van een installatie te benutten van de beschikbare middelen7. Dit resulteert in verlies van mobiele bronnen zoals nitraat die negatief van invloed kan zijn op de andere ecosystemen. De huidige wereldwijde stikstof gebruik efficiëntie wordt bijvoorbeeld geschat op 47%8. Betere resource gebruik efficiëntie via verbeterde managementmethoden en cultivars is daarom van groot belang voor zowel duurzame groei landbouwprodukten alsook wat betreft de duurzaamheid van het milieu. In deze context fabriek wortels worden beschouwd als een kerndoel voor verbeterde gewassen en bijsnijden systemen9,10.
Een tweede belangrijk achtergrond voor de recente belangstelling voor plantenwortels is technologische vooruitgang in meetmethoden. Root methoden hebben lang zijn beperkt door twee belangrijke uitdagingen: voor meting van de wortels van planten die groeien in de bodem worden geïsoleerd voor kwantificering moesten, meestal door het wassen van11, gevolg daarvan verstoringen ondergaat de architecturale rangschikking van wortel assen. In-situ wortel observatie met behulp van de opgraving methoden, waardoor het behoud van de natuurlijke ligging van de wortels in de bodem, voor Botanische beschrijving12zijn gebruikt. Nog steeds zijn zeer tijdrovend en dus niet voldoen aan de eisen van de doorvoer van vergelijkende structurele-functionele wortelsysteem analyse. Aan de andere kant werden hoge gegevensdoorvoer methoden voor root het platform meting meestal gedaan op kunstmatige media en voor seedling planten13 waar de extrapolatie naar de omgeving van de natuurlijke groei van planten is twijfelachtig14.
De recente boom van wortel onderzoek is nauw verbonden met het voorschot in imaging methoden15. Imaging benaderingen in wortel studies kan ruwweg worden ingedeeld in drie typen. Ten eerste zijn er hoge resolutie 3D methoden zoals CT en MRI16. Deze methoden zijn meest geschikt om interactie processen van plantenwortels met grond, te bestuderen, zoals droogte veroorzaakte xylem longembolie17. Meestal worden ze toegepast op relatief kleine steekproeven waar zij gedetailleerde opmerkingen maken. Een vergelijking van CT en MRI voor verschillend formaat potten en fijne wortel imaging is beschikbaar in18. Ten tweede, zijn er high-throughput beeldvormende methoden19,20. Deze methoden zijn meestal op basis van gemeenschappelijke 2D RGB beeldvorming van wortels groeien op kunstmatige media (gel, kiemkracht papier) waar hoog contrast kan relatief eenvoudig dissectie tussen wortels en achtergrond. Ze zijn geschikt voor hoge doorvoer vergelijking tussen zaailing wortel eigenschappen van verschillende gewas genotypen onder gestandaardiseerde kunstmatige groeiende voorwaarden13. Tussen deze twee benaderingen zijn rhizobox methoden: ze gebruiken 2D beeldvorming van wortels groeien in de bodem over langere periode en hebben middellange doorvoer21,22. Een recente uitdaging aan (2D) wortel imaging is te vangen ook indicatoren van wortel functionaliteit naast de beschrijving van de structuur23.
In het huidige document presenteren we de experimentele protocollen voor imaging-rhizobox root-systemen die gebruikmaken van (i) een goedkope en eenvoudige op maat gemaakte RGB imaging setup en (ii) een complexere NIR imaging setup gegroeid. Voorbeeld van de resultaten van deze twee opstellingen worden getoond en besproken in het kader van de plant fenotypering en de fysiologische plantenonderzoek.

<table>
	<tbody>
		<tr>
			<td>Rhizobox</td>
			<td>Technisches B&#252;ro f&#252;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

Beter begrip van de plant wortel dynamiek is essentieel voor de verbetering van de hulpbronnenefficiëntie gebruik van landbouwsystemen en verhogen de weerstand van gewas cultivars tegen milieu benadrukt. Een experimenteel protocol wordt gepresenteerd voor RGB- en Hyperspectrale beeldvorming van het wortelstelsel. De benadering wordt rhizoboxes gebruikt waar de planten groeien in natuurlijke grond over een langere tijd te observeren volledig ontwikkelde wortelstelsel. Experimentele instellingen zijn geïllustreerd voor het beoordelen van rhizobox planten onder waterstress en bestuderen van de rol van de wortels. Een RGB-imaging installatie is beschreven voor goedkope en snelle kwantificering van wortel ontwikkeling na verloop van tijd. Hyperspectrale beeldvorming verbetert de segmentering van de wortel van de achtergrond van de bodem in vergelijking met de RGB-kleur gebaseerd drempelmethode. De bijzondere kracht van Hyperspectrale beeldvorming is de overname van chemometric informatie over het systeem van de root-bodem voor functioneel begrip. Dit is aangetoond met hoge resolutie water inhoud mapping. Spectrale imaging is echter complexer in Beeldacquisitie, verwerking en analyse in vergelijking met de RGB-aanpak. Een combinatie van beide methoden kan een veelomvattende beoordeling gegeven van het wortelsysteem optimaliseren. Toepassingsvoorbeelden integratie van wortel en bovengrondse eigenschappen worden gegeven voor de context van plant fenotypering en planten fysiologisch onderzoek. Verdere verbetering van de beeldvorming van de wortel kan worden verkregen door het optimaliseren van de kwaliteit van de afbeelding RGB met betere verlichting met behulp van verschillende lichtbronnen en door uitbreiding van afbeelding analysemethoden op root zone-eigenschappen uit de spectrale gegevens afleiden.

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="Figure 8" class="xfigimg" src="/files/ftp_upload/56251/56251fig8.jpg" /> 
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="Figure 9" class="xfigimg" src="/files/ftp_upload/56251/56251fig9.jpg" /> 
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="Figure 10" class="xfigimg" src="/files/ftp_upload/56251/56251fig10.jpg" /> 
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="Figure 11" class="xfigimg" src="/files/ftp_upload/56251/56251fig11.jpg" /> 
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="Figure 12" class="xfigimg" src="/files/ftp_upload/56251/56251fig12.jpg" /> 
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="Figure 13" class="xfigimg" src="/files/ftp_upload/56251/56251fig13.jpg" /> 
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="Figure 14" class="xfigimg" src="/files/ftp_upload/56251/56251fig14.jpg" /> 
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="Figure 15" class="xfigimg" src="/files/ftp_upload/56251/56251fig15.jpg" /> 
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="Figure 16" class="xfigimg" src="/files/ftp_upload/56251/56251fig16.jpg" /> 
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="Figure 17" class="xfigimg" src="/files/ftp_upload/56251/56251fig17.jpg" /> 
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.

Watch this Scientific Journal Video about RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols at JoVE.com

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

RGB- en spectrale Root Imaging voor Plant fenotypering en fysiologisch onderzoek: experimentele opzet en Imaging protocollen

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