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

Författarna erkänner finansiering från österrikiska Science fond FWF via den projekt nummer P 25190-B16 (rötterna torka motstånd). Etablering av Hyperspektrala tänkbar infrastruktur stöddes ekonomiskt av den federala regeringen Niederösterreich (Land Niederösterreich) via projektet K3-F-282/001-2012. Ytterligare finansiering för sockerbetor experimentet mottogs från AGRANA forskning &#38; Innovation Center GmbH (Ulf). Författarna tackar Craig Jackson för teknisk support under experimentet och engelska korrigering av manuskriptet. Vi erkänner också Markus Freudhofmaier som bidragit till etableringen inställningen för RGB-imaging och Josef Schodl för byggandet av rhizobox montering.

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Författarna har något att avslöja.

Protokollen ger två kompletterande metoder för jord odlas rotsystem imaging. Ett kritiskt steg för tillförlitlig experimentella resultat är att fylla i den rhizoboxes som har att säkerställa en jämn och homogen substrat lager på frontglaset att ge snäva rot-smutsa kontakten på fönstret observation och undvika luftspalter. Detta är den främsta anledningen att använda jämförelsevis fina sållen jord av &#60; 2 mm: större aggregat resultera i högre ytan morfologi på fönstret observation med håligheter mellan aggregat. Förutom en högre risk för roten tip uttorkning kräver detta också mer komplex bild bearbetning tekniker för vatten mappning31.
Ändringar av protokollet därför fokusera på förbättrad och snabb fyllning av rhizoboxes. För närvarande påfyllningstid är cirka 30 minuter per låda. Vidare användning av rhizoboxes med två glas windows för avbildning från båda sidor och ändringar att optimera belysning homogenitet för bättre RGB-bilder är testade. Ytterligare maskinvara förlängning också överväga integrering av planar optodes32 samt kapacitans imaging33 in i rhizobox-systemet. Detta är dock utöver nuvarande uppgradering verksamhet.
Programvaruändringar fokusera på automatisk bildregistrering till säkring toppen och botten RBG bilder34. För Hyperspektrala metoder imaging avancerad oövervakade funktion utvinning28 samt känsligare övervakade mål identifieringsmetoder såsom SVMs35 testas. De Hyperspektrala data tillåter därmed potentiellt för bedömning av flera jord, odlingsbädden och rot boenden36. Dessutom är avsikten att utveckla en (halv) automatiserade programvara för rhizobox rot bilder baserat på en modifierad version av rotsystemet Analyzer37 att kvantifiera morfologiska (längd, diameter, yta) samt arkitektoniskt drag (förgrening frekvens, förgrening vinklar).
Den största begränsningen av protokollet jämfört 3D imaging tillvägagångssätt är begränsningen till ytan synliga rot och odlingsbädden boenden. Det har dock visats att de synliga rot drag är en pålitlig proxy för den hela rotsystem21. Den rhizobox tekniken är enkelt kombineras med traditionella destruktiva provtagning (tvättning) i slutet av dynamisk tillväxt imaging för att validera förhållandet av synliga vs. totala rotsystem drag. Som denna relation kan variera bland arter21, rekommenderas destruktiva provtagning att garantera tillförlitlig inferens från synliga egenskaper för de nya fenotypning med en annan gröda arter.
Den viktigaste fördelen med av protokollet som presenteras här är kombinationen av realistiska odlingsförhållanden (jord), relativt höga potentiella genomflödet för temporally löst RGB-imaging och inferens på roten funktionalitet (t.ex. upptag av vatten) via chemometric rot och odlingsbädden data från Hyperspektrala imaging. Därmed metoderna som övervinner inferens begränsningar i hög genomströmning plantan och icke-smutsa rot imaging metoder14, medan det kan delvis djupa fenotypning insikter i funktionella processer med mindre experimentella komplexitet och högre dataflöde jämfört med avancerade 3D metoder15.
I kommande experiment protokollet används för att studera effekten av mykorrhiza på rotsystemet utveckling och funktionalitet av baljväxter samt när det gäller fenotypning rot kännetecken för täcka grödan arter i förhållande till markens struktur, kväve och kol cykling.

Rötterna ger flera viktiga funktioner för växter såsom lagring av assimilerar, ankringen av landlevande växter i jord, och upptag och transport av vatten och näringsämnen1. Ur en evolutionär synvinkel anses bildandet av roten axlar vara en grundläggande förutsättning för beskärningen av mark växter2. Trots denna viktiga roll har historiskt rötter ockuperat endast en marginell position inom biologisk forskning. I nyare tider, men ökar det vetenskapliga intresset växt rotsystem som framgår i figur 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
Figur 1: Betydelsen av roten studier i botanik. Antal root relaterade studier som en procentandel av alla publicerade växt studier i SCI tidskrifter under de senaste årtiondena. Sökresultat från Scopus med sökord ”anläggning” och ”planterar och rot”. <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Klicka här för att se en större version av denna siffra.</a>
Två anledningar kan vara hypotesen att ligger bakom de senaste framstegen inom rot forskning. Första, terrestrial vegetationen utsätts oftare yttre påfrestningar till följd av globala förändringar3. I samband med vegetabiliska jordbruksprodukter produktion uppskattas det att globalt cirka 30% av jordbruksarealen begränsas av vatten och fosfor4,5. Stressreducering av skördarna är en huvudorsak för betydande avkastning luckor som är globalt uppskattas lägre 50 procent av potentiell produktivitet för rainfed jordbruksekosystemen6. Förutom låg resurstillgänglighet, är detta också relaterat till dålig resurseffektivitet användning, dvs otillräcklig kapacitet av en växt att utnyttja tillgängliga resurser7. Detta resulterar i förluster av mobila resurser såsom nitrat som kan inverka negativt på andra ekosystem. Den nuvarande globala kväve effektiviteten beräknas till exempel till 47%8. Bättre resursanvändning effektivitet via metoder för förbättrad hantering och sorter är därför av stor betydelse för både uthållig tillväxt av jordbruks utgångar samt när det gäller miljömässig hållbarhet. I detta sammanhang växten anses rötter vara ett huvudmål för förbättrade grödor och beskärning system9,10.
En andra viktig bakgrund för det senaste intresset för växternas rötter är tekniska framsteg i mätmetoder. Root metoder har länge begränsats av två viktiga utmaningar: för mätning av rötter från växter som växer i jord som de hade att vara isolerad för kvantifiering, mestadels genom att tvätta11, därmed störa det arkitektoniska arrangemanget av roten axlar. In situ - roten observation med utgrävning metoder, därmed bevara den naturliga platsen för rötter i jord, har använts för botaniska Beskrivning12. Fortfarande de är mycket tidskrävande och därmed att kraven inte genomströmning av jämförande strukturella-funktionella rotsystem analys. Å andra sidan var hög genomströmning metoder för roten arkitekturen mätning mestadels gjort på konstgjord medier och för plantor växter13 där en extrapolering av växter naturliga tillväxt miljön är tvivelaktiga14.
Den senaste högkonjunkturen rot forskning är tätt knuten till förskottet i imaging metoder15. Imaging metoder i roten studier kan grovt grupperas i tre typer. Först finns det högupplösta 3D metoder såsom CT och Mr16. Dessa metoder är mest lämpade att studera interaktion processer av växternas rötter med jord, såsom torka inducerad xylem embolism17. Vanligtvis används de på jämförelsevis små prover där de tillåter detaljerade observationer. En jämförelse av CT och MRT för olika stora krukor och fina rot avbildning finns i18. För det andra finns det hög genomströmning bildgivande metoder19,20. Dessa metoder är mestadels baserade på gemensamma 2D RGB avbildning av rötter växa på konstgjorda media (gel, grobarhet papper) där hög kontrast gör jämförelsevis enkel dissektion mellan rötter och bakgrund. De är lämpliga för hög genomströmning jämförelse bland fröplanta rot drag av olika grödor genotyper enligt standardiserade artificiella växande villkor13. Mellan dessa två metoder är metoder som rhizobox: de använder 2D avbildning av rötter som växer i jord under längre tidsperiod och har medelstora genomströmning21,22. En senaste utmaning till (2D) roten imaging är att fånga även indikatorer på roten funktionalitet förutom Beskrivning av struktur23.
I detta dokument presenterar vi de experimentella protokoll för imaging rhizobox växt rot-system som använder en billig och enkel skräddarsydda RGB imaging installationsprogrammet och (ii) en mer komplex NIR imaging. Exempel resultat från dessa två inställningar är visas och diskuteras i samband med anläggningen fenotypning och fysiologiska växtforskning.

<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

Bättre förståelse av växten rot dynamics är nödvändigt att förbättra resurseffektiviteten användning av jordbrukssystem och öka motståndet av gröda sorter mot miljöpåfrestningar. Ett experimentellt protokoll presenteras för RGB och Hyperspektrala avbildning av rotsystem. Metoden används rhizoboxes där växter växer i naturlig jord över en längre tid att observera fullt utvecklade rotsystem. Experimentella inställningarna exemplifieras för att bedöma rhizobox växter under vattenstress och studerar roll för rötter. En RGB-imaging installation beskrivs för billig och snabb kvantifiering av roten utveckling över tid. Hyperspektrala imaging förbättrar rot segmentering från jord bakgrunden jämfört med RGB-färg baserat tröskelvärde. Hyperspektrala imaging viss styrka är förvärvet av chemometric information om rot-smutsa systemet för funktionell förståelse. Detta visas med hög upplösning vatten innehåll kartläggning. Spectral imaging är emellertid mer komplex bild förvärvandet, bearbetning och analys jämfört med metoden RGB. En kombination av båda metoderna kan optimera en omfattande bedömning av rotsystemet. Applikationsexempel integrera rot och aboveground egenskaper ges för ramen för fenotypning och växters fysiologiska växtforskning. Ytterligare förbättring av roten imaging kan erhållas genom att optimera RGB bildkvalitet med bättre belysning med olika ljuskällor och i förlängningen av bild analysmetoder att sluta på roten zonegenskaper från spektrala 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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