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

Forfatterne anerkende finansieringen fra den østrigske videnskab fond FWF via projektet nummeret P 25190-B16 (rødder af tørke resistens). Etablering af hyperspectral imaging infrastruktur blev støttet økonomisk af den føderale regering i Niederösterreich (Land Niederösterreich) via projektet K3-F-282/001-2012. Yderligere finansiering for sukkerroer eksperiment blev modtaget fra AGRANA forskning &#38; Innovation Center GmbH (ERLENDS). Forfatterne tak Craig Jackson for teknisk support i løbet af eksperimentet og Dansk korrektion af manuskriptet. Vi anerkender også Markus Freudhofmaier der har bidraget til oprettelsen RGB imaging setup og Josef Schodl for konstruktion af rhizobox montering.

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Forfatterne har ikke noget at oplyse.

Protokollerne giver to komplementære tilgange til jord dyrkes rodsystem billeddannelse. Et kritisk trin for pålidelige eksperimentelle resultater påfyldning af den rhizoboxes, der skal sikre en jævn og ensartet substrat lag på front glasset til at give stramme root-jord kontakt på vinduet observation og undgå lufthuller. Dette er hovedårsagen til brug forholdsvis fint sigtede jord af &#60; 2 mm: større aggregater resultere i højere overflade morfologi på vinduet observation med hulrum mellem aggregater. Udover en højere risiko for root tip dehydrering kræver dette også mere komplekse billede behandling teknikker for vand kortlægning31.
Ændringer af protokollen derfor fokus på forbedret og hurtig påfyldning af rhizoboxes. I øjeblikket fylder tid er omkring 30 minutter pr kasse. Desuden brug af rhizoboxes med to glas windows imaging fra begge sider og ændringer til at optimere belysning ensartethed for bedre RGB-billeder er testet. Yderligere hardware udvidelse kan også overveje integration af planar optodes32 samt kapacitans imaging33 i rhizobox systemet. Dette er dog ud over aktuelle opgradering aktiviteter.
Software ændringer fokusere på automatiske billede registrering at sammensmelte top og bund RBG billeder34. For hyperspectral tilgange billeddannelse avanceret uovervåget funktion udvinding28 samt mere følsomme detektionsmetoder i overvåget mål såsom SVMs35 er testet. Derved mulighed hyperspectral data potentielt for vurdering af flere jord, rhizosfære og rod egenskaber36. Desuden er hensigten at udvikle et (semi) automatiseret software til rhizobox root billeder baseret på en modificeret version af rodsystemet Analyzer37 at kvantificere morfologiske (længde, diameter, overflade) samt arkitektoniske træk (forgrening frekvens, forgrening vinkler).
Den største begrænsning af protokollen i forhold til 3D imaging tilgange er begrænsning til overflade synlig rod og rhizosfære egenskaber. Men det er blevet påvist, at den synlige rod træk er en pålidelig proxy for hele rodsystemet21. Rhizobox teknikken er let kombineres med traditionelle destruktiv prøvetagning (vask) i slutningen af dynamisk vækst imaging for at validere relationen for synlige vs samlede rodsystem træk. Da denne relation kan variere blandt arter21, er destruktiv prøvetagning anbefales for at sikre pålidelig inferens fra synlige træk for alle nye fænotyper serien med en anden afgrøde arter.
Den vigtigste fordel af protokollen præsenteres her er kombinationen af realistisk vækstbetingelser (jord), relativt høje potentielle overførselshastighed for tidsligt løst RGB imaging og inferens på roden funktionalitet (f.eks. vand optagelse) via chemometric rod og rhizosfære data fra hyperspectral billeddannelse. Dermed metoderne overvinder inferens restriktioner i høj overførselshastighed sætteplante og ikke-jorden roden imaging metoder14, mens det delvis giver dyb fænotyper indsigt i funktionelle processer med mindre eksperimentelle kompleksitet og højere produktivitet i forhold til avanceret 3D metoder15.
I kommende eksperimenter protokollen vil blive brugt til at studere effekten af mykorrhiza på rodsystem udvikling og funktionalitet af bælgfrugter såvel som for fænotyper root Karakteristik af cover afgrøde arter i forhold til jordens struktur, kvælstof og kulstof cykling.

Rødderne giver flere væsentlige funktioner for planter såsom opbevaring af sidestiller, forankring af landplanter i jord, og optagelse og transport af vand og næringsstoffer1. Fra et evolutionært synspunkt, er dannelsen af roden akser betragtes som en grundlæggende forudsætning for oprindelsen af jord planter2. På trods af denne vigtige rolle, har historisk rødder besat kun en marginal position i biologiske forskning. I nyere tid, der dog stigende videnskabelig interesse i anlægget rod systemer som det fremgår i figur 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
Figur 1: Relevansen af roden undersøgelser i anlægget videnskaber. Antallet af root relaterede studier som en procentdel af alle offentliggjorte plante undersøgelser i SCI tidsskrifter i de sidste årtier. Søgeresultat fra Scopus ved hjælp af søgeord "plante" og "plante og rod". <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Venligst klik her for at se en større version af dette tal.</a>
To vigtigste grunde kan være hypotese at ligge til grund for de seneste fremskridt i roden forskning. Første, terrestriske vegetation er udsat for mere hyppige miljøbelastninger som følge af global ændring3. I forbindelse med landbrugets planteavl anslås det, at globalt omkring 30% af landbrugsarealet er begrænset af vand og fosfor4,5. Stress reduktion af høstudbyttet er en hovedårsag til betydelige afkast huller, der er globalt anslås til lavere 50% af potentielle produktivitet til rainfed agro-økosystemer6. Udover lav ressourcetilgængelighed, er det også relateret til dårlig brug ressourceeffektivitet, dvs. utilstrækkelig kapacitet for et anlæg til at udnytte tilgængelige ressourcer7. Dette resulterer i tab af mobile ressourcer som nitrat, der kan indvirke negativt på andre økosystemer. Den nuværende globale kvælstof brug effektivitet anslås for eksempel til 47%8. Bedre ressource bruger effektivitet via forbedrede ledelsesmetoder og kultivarer er derfor af stor betydning for både vedvarende vækst af landbrugets output samt med hensyn til miljømæssig bæredygtighed. I denne forbindelse plante anses rødder for at være et centralt mål for forbedrede afgrøder og beskæring systemer9,10.
En anden vigtig baggrund for den nylige interesse i planternes rødder er teknologiske fremskridt inden for målemetoder. Roden metoder har længe været begrænset af to væsentlige udfordringer: for måling af rødder fra planter, der vokser i jord de skulle isoleres for kvantificering, det meste af vask11, dermed forstyrre den arkitektoniske arrangement af roden akser. In-situ root observation ved hjælp af udgravningen metoder, dermed bevare den naturlige placering af rødderne i jorden, har været brugt til Botanisk beskrivelse12. Stadig de er meget tidskrævende og opfylder således ikke kravene gennemløb af sammenlignende struktur-funktionelle rodsystem analyse. På den anden side høj overførselshastighed metoder til roden arkitektur måling var for det meste udført på kunstige medier og for sætteplante planter13 hvor ekstrapolation til den naturlige vækstmiljø af planter er tvivlsom14.
Den seneste boom af roden forskning er tæt knyttet til advance i imaging metoder15. Imaging tilgange i roden undersøgelser kan groft inddeles i tre typer. Først er der høj opløsning 3D metoder såsom CT- og Mr16. Disse metoder er mest egnet til at studere samspillet processer af planternes rødder med jord, såsom tørke induceret vedvævet emboli17. De anvendes typisk til forholdsvis små prøver, hvor de giver detaljerede observationer. En sammenligning af CT og Mr for forskellige størrelser gryder og fine root imaging tilbydes i18. Andet er der høj overførselshastighed Billeddannende metoder19,20. Disse metoder er hovedsagelig baseret på fælles 2D RGB billeddannelse af rødder vokser på kunstige medier (gel, spiring papir) hvor høj kontrast gør det forholdsvis simpelt dissektion mellem rødder og baggrund. De er egnede til høj overførselshastighed sammenligning blandt sætteplante root træk af forskellige afgrøde genotyper under standardiserede kunstige voksende betingelser13. I mellem disse to tilgange er rhizobox metoder: de bruger 2D billeddannelse af rødder vokser i jorden over længere periode og har medium gennemløb21,22. En nylig udfordring at (2D) roden imaging er at fange også indikatorer af rod funktioner ud over beskrivelsen af struktur23.
I den nuværende papir præsenterer vi de eksperimentelle protokoller for imaging rhizobox vokset root systemer ved hjælp af en billig og enkel custom-made RGB imaging setup og (ii) en mere kompleks NIR imaging setup. Eksempel resultater fra disse to opsætninger er vist og diskuteret i forbindelse med anlægget fænotyper og fysiologiske planteforskning.

<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

Bedre forståelse af anlægget rod dynamics er afgørende at forbedre ressourceeffektiviteten brug af landbrugssystemer og øge modstanden i afgrøde sorter mod miljøbelastninger. En eksperimentel protokol er præsenteret for RGB og hyperspectral billeddannelse af rodsystemer. Metoden bruger rhizoboxes hvor planter vokser i naturlig jord over en længere tid til at observere fuldt udviklede rodsystemer. Eksperimentelle indstillinger er eksemplificeret for vurderingen af rhizobox planter under vand stress og studere rollen af rødder. Et RGB-imaging setup er beskrevet til billig og hurtig kvantificering af roden udvikling over tid. Hyperspectral imaging forbedrer root segmentering fra jord baggrunden i forhold til RGB farve baseret tærskel. Den særlige styrke hyperspectral Imaging er erhvervelse af chemometric oplysninger på root-jord system for funktionel forståelse. Dette er påvist med høj opløsning vand indhold kortlægning. Spektrale imaging men er mere kompleks i image erhvervelse, behandling og analyse i forhold til RGB-tilgang. En kombination af begge metoder kan optimere en omfattende vurdering af rodsystemet. Applikationseksempler integrere rod og overjordiske træk får for forbindelse med plante fænotyper og plante fysiologiske forskning. Yderligere forbedring af roden imaging kan opnås ved at optimere RGB-billedkvalitet med bedre belysning ved hjælp af forskellige lyskilder og i forlængelse af billede analysemetoder til at udlede på roden zoneegenskaber fra spektrale 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="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

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