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 bekrefter støtte fra den østerrikske Science fondet FWF via Project antallet P 25190-B16 (røttene av tørke motstand). Etablering av hyperspektral tenkelig infrastruktur ble støttet økonomisk av den føderale regjeringen Niederösterreich (Land OSTÖSTERREICH) via prosjektet K3-F-282/001-2012. Ytterligere finansiering for sukkerroer eksperimentet ble mottatt fra AGRANA forskning &#38; innovasjon Center GmbH (ARIC). Forfatterne takker Craig Jackson kundestøtte under eksperimentet og engelsk korrigering av manuskriptet. Vi erkjenner også Markus Freudhofmaier som har bidratt til etableringen RGB tenkelig oppsettet og Josef Schodl for bygging av rhizobox montering.

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Forfatterne ikke avsløre.

Protokollene gir to utfyllende tilnærminger for jord vokst rotsystem bildebehandling. Et kritisk punkt for pålitelig eksperimentelle resultater er fylt av rhizoboxes som skal sikre en jevn og homogen substrat laget på glassfront stramt rot-jord kontakt i vinduet observasjon og unngå luft hull. Dette er hovedgrunnen til bruker relativt fint soldet jord av &#60; 2 mm: større aggregater resultere i høyere overflaten morfologi i vinduet observasjon med tomrom mellom aggregat. Foruten en høyere risiko for rot tips dehydrering krever dette også mer komplekse bildebehandlingen teknikker for vann kartlegging31.
Endringer av protokollen derfor fokusere på forbedret og rask fylling av rhizoboxes. Øyeblikket fyller tid er ca 30 minutter per boks. Videre bruk av rhizoboxes med to glassvinduer tenkelig fra begge sider og modifikasjoner optimere belysning homogenitet for bedre RGB-bilder er testet. Ytterligere maskinvare utvidelse kan også vurdere integrering av Plane optodes32 samt kapasitans imaging33 i rhizobox systemet. Dette er imidlertid utenfor gjeldende oppgradering aktiviteter.
Software modifikasjoner fokus på automatisk bilde registrering sikringen toppen og bunnen RBG bilder34. For hyperspektral tilnærminger tenkelig avanserte unsupervised egenskapsuttrekking28 samt mer følsomme veiledet målet gjenkjenningsmetoder som SVMs35 testes. Dermed tillate hyperspektral dataene potensielt vurdering av flere jord, rhizosphere og root egenskaper36. Videre er det ment å utvikle en (semi) lager disponering programvare for rhizobox rot bilder basert på en modifisert versjon av rotsystem Analyzer37 å kvantifisere morfologiske (lengde, diameter, overflaten) og arkitektoniske trekk (forgrening frekvens, forgrening vinkler).
Den viktigste begrensningen av protokollen sammenlignet med 3D tenkelig tilnærminger er begrensningen til overflaten synlig og rhizosphere egenskaper. Det har imidlertid vist at den synlige rot trekk er en pålitelig proxy for hele rotsystem21. Rhizobox teknikken er enkelt kombinert med tradisjonell destruktive sampling (vask) på slutten av dynamisk vekst imaging for å validere for synlig vs totale rotsystem trekk. Denne relasjonen kan variere blant arter21, anbefales destruktive prøvetaking å sikre pålitelig slutning fra vises egenskaper for alle nye phenotyping serien med et annet beskjære arter.
Den viktigste fordelen av protokollen presenteres her er kombinasjonen av realistiske vekstforhold (jord), relativt høye potensielle gjennomstrømming for timelig løst RGB bildebehandling og inferens på roten funksjonalitet (f.eks vann opptak) via chemometric rot og rhizosphere data fra hyperspektral avbildning. Dermed metodene overvinner slutning begrensninger i høy gjennomstrømning frøplante og ikke-jord rot imaging metoder14, mens den delvis innrømmer dypt phenotyping innsikt i funksjonelle prosesser med mindre eksperimentelle kompleksitet og høyere ytelse sammenlignet med avansert 3D metoder15.
Kommende eksperimenter protokollen skal brukes til å studere effekter av mycorrhiza på roten systemutvikling og funksjonalitet av belgfrukter så vel som for phenotyping rot kjennetegner cover beskjære arter jord struktur, nitrogen og karbon sykling.

Røtter gir flere viktige funksjoner for planter som lagring av assimilates, anchorage terrestrial anlegg i jord, og opptak og transport av vann og næringsstoffer1. Fra et evolusjonært synspunkt, dannelsen av rot akser er ansett som en grunnleggende forutsetning for opprinnelsen til land planter2. Til tross for denne viktige rollen, har historisk røtter okkupert bare en marginale stilling i biologisk forskning. I nyere tid, er det imidlertid økende vitenskapelig interesse i plante rot systemer som vises i figur 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
Figur 1: Relevans av rot studier i anlegget sciences. Antall rot relaterte studier som en prosent av alle publiserte anlegget studier i SCI journaler de siste tiårene. Søkeresultat fra Scopus søkeord "anlegget" og "plante og root". <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Klikk her for å se en større versjon av dette tallet.</a>
To hovedårsaker kan være antatt ligger den siste utviklingen i roten forskning. Første, terrestrial vegetasjon er utsatt for hyppigere miljømessige påkjenninger som følge av global endring3. I forbindelse med landbruket planteproduksjon anslås det at globalt rundt 30% av dagens jordbruksarealer er begrenset av vann og fosfor4,5. Stress reduksjon av avlinger er hovedgrunnen for betydelig avkastning gapene som globalt anslås til lavere 50% av potensielle produktivitet for rainfed agro-økosystemer6. Foruten lav ressurstilgjengelighet, er dette også knyttet til dårlig ressurs bruk effektivitet, dvs nok kapasitet av en plante å utnytte ressurser7. Dette resulterer i tap av mobile ressurser som nitrat som kan negativt påvirke andre økosystemer. Gjeldende globale nitrogen bruk effektiviteten anslås for eksempel til 47%8. Bedre ressurs bruker effektivitet via bedre forvaltning metoder og kultivarer er derfor av høy betydning for både vedvarende vekst av landbruket utganger så vel som for miljømessig bærekraft. I denne sammenheng plante anses røtter å være et viktig mål for bedre avlinger og beskjæring systemer9,10.
Andre viktig bakgrunn for siste interessen planterøttene er teknologiske fremskritt i målemetoder. Root metoder har lenge vært begrenset av to sentrale utfordringer: for måling av røtter fra planter vokser i jord måtte de være isolert for kvantifisering, hovedsakelig ved å vaske11, og dermed urovekkende arkitektoniske ordningen av rot akser. In situ rot observasjon bruker utgravning metoder, og dermed spare naturlig plasseringen av røtter i jord, brukes for botanisk beskrivelse12. Fortsatt de er svært tidkrevende og dermed oppfyller ikke gjennomstrømningen kravene i sammenlignende strukturelle-funksjonelle rotsystem analyse. Derimot var høy gjennomstrømming metoder for rot arkitektur måling det meste gjort på kunstig media og frøplante planter13 der ekstrapolering for det naturlige miljøet av planter er tvilsom14.
Den siste bommen rot forskning er tett knyttet til forhånd i imaging metoder15. Imaging tilnærminger i roten studier kan grovt grupperes i tre typer. Først er det høy resolution 3D metoder som CT og MRI16. Disse metodene er best egnet til å studere samspillet prosesser av plante røtter med jord, slik som tørke indusert vedvev embolism17. Vanligvis brukes de på relativt små utvalg der de gi detaljert observasjoner. En sammenligning av CT og MRI for annerledes størrelse Potter og fine rot bildebehandling tilbys i18. Andre er det høy gjennomstrømming tenkelig metoder19,20. Disse metodene er hovedsakelig basert på felles 2D RGB avbilding av røtter vokser på kunstig media (gel, spiring papir) hvor høy kontrast gir relativt enkel disseksjon mellom røtter og bakgrunn. De er egnet for høy gjennomstrømning sammenligning mellom frøplante rot personlighetstrekk av forskjellige beskjære genotyper under standardisert kunstig voksende forhold13. Mellom disse to tilnærmingene er rhizobox metoder: de bruker 2D avbilding av røtter vokser i jord over lengre tid og har medium gjennomstrømming21,22. En siste utfordring (2D) root bildebehandling er å fange også indikatorer rot funksjonalitet i tillegg til beskrivelse av strukturen23.
I denne utredningen presenterer vi eksperimentelle protokollene for imaging rhizobox vokst rotsystem (i) en billig og enkel skreddersydd RGB tenkelig oppsett og (ii) en mer kompleks NIR tenkelig oppsett. Eksempel resultatene fra disse to oppsett er vist og omtalt i forbindelse med anlegget phenotyping 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

Forståelse av anlegget rot dynamics er viktig å forbedre ressurseffektiviteten for bruk av jordbruks-systemer og øke motstanden av avling kultivarer mot miljømessige belastninger. En eksperimentell protokoll er presentert for RGB og hyperspektral avbildning med rotsystem. Fremgangsmåten bruker rhizoboxes der planter vokse i naturlige jord over lengre tid å observere fullt utviklet rotsystem. Eksperimentell innstillinger er eksemplifisert for vurdering rhizobox planter under vann stress og studere rollen røtter. En RGB tenkelig oppsett er beskrevet for billig og rask kvantifisering av root utvikling over tid. Hyperspektral avbildning forbedrer rot segmentering av jord bakgrunnen forhold til RGB fargen basert terskelverdi. Bestemt styrke hyperspektral avbildning er oppkjøpet av chemometric informasjon på rot-jord system for funksjonell forståelse. Dette er demonstrert med høy oppløsning vann innhold kartlegging. Spectral imaging men er mer komplisert i bildeopptak, bearbeiding og analyse forhold til RGB tilnærming. En kombinasjon av begge metoder kan optimalisere en helhetlig vurdering av rotsystem. Programmet eksempler integrere rot og aboveground trekk gis for konteksten til anlegget phenotyping og plante fysiologiske forskning. Ytterligere forbedring av rot bildebehandling kan fås ved å optimalisere RGB bildekvaliteten med bedre belysning med forskjellige lyskilder og dermed bildet analyse metoder for å utlede på roten nskaper fra spektraldata.

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 og Spectral rot bildebehandling for anlegget Phenotyping og fysiologiske forskning: eksperimentelle oppsett og Imaging protokoller

En eksperimentell protokoll er presentert for vurdering av jord dyrket plante rot systemer med RGB og hyperspektral avbildning. Kombinasjonen av RGB bilde tid serien med chemometric informasjon fra hyperspektral skanner optimaliserer innsikt i anlegget rot dynamics.

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