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

Os autores reconhecem financiamento do austríaco ciência fundo FWF através do projeto número P 25190-B16 (as raízes da resistência à seca). Estabelecimento da infra-estrutura de imagens hiperespectrais foi apoiado financeiramente pela Federal governo da Baixa Áustria (Niederösterreich terra) através do projeto K3-F-282/001-2012. Financiamento adicional para o experimento de beterraba foi recebido da pesquisa AGRANA &#38; GmbH de centro de inovação (ARIC). Os autores agradecer Craig Jackson para suporte técnico durante o experimento e inglês correção do manuscrito. Também reconhecemos Markus Freudhofmaier que contribuíram para o estabelecimento da configuração de imagem RGB e Josef Schodl para construção de montagem rhizobox.

<ol>
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Os autores não têm nada para divulgar.

Os protocolos fornecem duas abordagens complementares para o solo cultivado de imagem de sistema radicular. Um passo crítico para resultados experimentais confiáveis está enchendo do rhizoboxes que tem que garantir uma camada homogênea e até mesmo substrato no vidro da frente para proporcionar contato solo-raiz apertado a janela de observação e evitar lacunas de ar. Esta é a principal razão para usar o solo peneirado comparativamente fino de &#60; 2 mm: agregados maiores resultam em morfologia de superfície superior a janela de observação com vazios entre os agregados. Além de um maior risco de desidratação de dica de raiz, isso também requer mais complexas técnicas de processamento de imagem para mapeamento de água31.
Modificações do protocolo, portanto, focar melhor e rápido enchimento de rhizoboxes. Tempo de enchimento atualmente é cerca de 30 minutos pela caixa. Além do uso de rhizoboxes com duas janelas de vidro para a imagem latente de ambos os lados e modificações para otimizar a homogeneidade de iluminação para imagens RGB melhores são testados. Nova prorrogação de hardware também pode considerar a integração de planar optodes32 , bem como da capacidade de imagem33 no sistema rhizobox. No entanto, esta é além de atividades de atualização atuais.
Modificações de software focar o registo automático da imagem para fundir a parte superior e inferior RBG fotos34. Para hiperespectrais extração de recurso sem supervisão avançado de imagem aproxima-se28 , bem como métodos de deteção de destino supervisionados mais sensíveis tais como SVMs35 são testados. Assim, os dados hiperespectrais potencialmente permitem a avaliação de múltiplas Propriedades rizosfera, solo e raiz36. Além disso pretende-se desenvolver um (semi) automatizados software para rhizobox raiz imagens com base em uma versão modificada do sistema radicular Analyzer37 quantificar morfológica (comprimento, diâmetro, superfície), bem como traços arquitectónicos (frequência de ramificação, ângulos de ramificação).
A principal limitação do protocolo em comparação com abordagens de imagem 3D é a restrição à superfície visível raiz e propriedades da rizosfera. No entanto foi demonstrado que os traços visíveis de raiz são um proxy confiável para o sistema inteiro da raiz21. A técnica de rhizobox é facilmente combinada com tradicional amostragem destrutiva (lavagem) no final do crescimento dinâmico da imagem latente para validar a relação de visível vs traços do sistema total da raiz. Como esta relação pode variar entre espécies21, amostragem destrutiva é recomendada para assegurar uma conclusão confiável de traços visíveis para qualquer nova série fenotipagem com uma espécie de diferentes culturas.
A principal vantagem do protocolo aqui apresentado é a combinação de condições de crescimento realistas (solo), relativamente alto rendimento potencial para a imagem RGB temporalmente resolvido e inferência na funcionalidade de raiz (por exemplo, absorção de água) via os dados de raiz e rizosfera quimiométricas de imagens hiperespectrais. Desse modo, os métodos supera restrições de inferência em plântulas de alto rendimento e raiz não-solo de imagem métodos14, enquanto parcialmente permite insights profundos fenotipagem em processos de funcionais com complexidade menos experimental e maior rendimento em comparação com métodos avançados de 3D15.
Nas próximas experiências o protocolo será usado para estudar o efeito de micorriza no desenvolvimento do sistema radicular e a funcionalidade de leguminosas, bem como para as características de raiz de fenotipagem de espécie de cultura de cobertura em relação à estrutura do solo, nitrogênio e carbono ciclismo.

Raízes fornecem várias funções essenciais para as plantas tais como armazenamento de assimila, fixação de plantas terrestres no solo e a absorção e o transporte de água e nutrientes1. Do ponto de vista evolutivo, a formação dos eixos de raiz é considerada uma condição fundamental para a origem da terra plantas2. Apesar deste importante papel, historicamente raízes ocuparam apenas uma posição marginal na pesquisa biológica. Em tempos mais recentes, no entanto, existe uma crescente interesse científico em sistemas de raiz de planta como evidenciado na Figura 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
Figura 1: Relevância de estudos de raiz em Ciências de planta. Número de raiz relacionados com estudos como uma porcentagem de todos os estudos de planta publicada em revistas do SCI nas últimas décadas. Pesquisar resultado da Scopus usando palavras-chave "planta" e "planta e raiz". <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">Clique aqui para ver uma versão maior desta figura.</a>
Dois principais motivos podem ser a hipótese de fundamentam os recentes avanços na pesquisa de raiz. Vegetação terrestre, primeira é exposta a estresses ambientais mais frequentes, como resultado da mudança global3. No contexto da produção de culturas agrícolas estima-se que globalmente cerca de 30% da área agrícola são limitados por água e fósforo4,5. Redução do estresse de colheitas são uma razão principal para rendimento significativas lacunas que são estimados globalmente em menor 50% da produtividade potencial de sequeiro agro-ecossistemas6. Além de disponibilidade de recursos baixos, isso também está relacionado à eficiência de uso recurso pobre, ou seja, insuficiente capacidade de uma planta para explorar os recursos disponíveis7. Isso resulta em perdas de recursos móveis, tais como o nitrato, que pode afetar negativamente a outros ecossistemas. Por exemplo, a eficiência de uso de nitrogênio global atual é estimada em 47%8. Usar o recurso melhor eficiência através de métodos de melhoria da gestão e cultivares, portanto, de grande importância para ambos sustentado crescimento dos produtos agrícolas, bem como para a sustentabilidade ambiental. Nesta fábrica de contexto raízes são consideradas para ser um alvo chave para melhoria de culturas e cultivo sistemas9,10.
Um segundo fundo importante para o interesse recente em raízes de plantas é o avanço tecnológico nos métodos de medição. Métodos raiz tempo tem sido restrito pelos dois principais desafios: para medição de raízes de plantas que crescem no solo, eles tiveram que ser isolados para quantificação, principalmente lavando11, perturbando o arranjo arquitectónico dos eixos de raiz. Observação de raiz in situ utilizando escavação métodos, conservando assim a localização natural das raízes no solo, têm sido utilizados para descrição botânica12. Ainda são muito demorados e, portanto, não atende aos requisitos de taxa de transferência de análise comparativa estrutural-funcional sistema de raiz. Por outro lado elevado-throughput métodos para medição de arquitectura de raiz foram feitos principalmente em meios artificiais e para mudas de plantas13 onde a extrapolação para o ambiente natural de crescimento das plantas é questionável14.
O crescimento recente da investigação de raiz é fortemente ligado ao avanço em métodos15por imagens. Abordagens em estudos de raiz de imagem aproximadamente pode ser agrupado em três tipos. Primeiro, existem métodos 3D de alta resolução como CT e MRI16. Esses métodos são mais adequados para estudar processos de interação das raízes de plantas com o solo, tais como secas induzidas pelo xilema embolia17. Normalmente são aplicados à comparativamente pequenas amostras onde eles permitem observações detalhadas. Uma comparação de CT e MRI para potes de diferente tamanhos e raiz fina imagem é fornecida em18. Em segundo lugar, há elevado-produção de imagem métodos19,20. Esses métodos são na maior parte com base em comum 2D RGB imagens de raízes crescendo em meios artificiais (gel, papel de germinação) onde o alto contraste permite comparativamente simples dissecação entre raízes e plano de fundo. Eles são adequados para a comparação de alta taxa de transferência entre os traços de raiz de mudas de genótipos diferentes culturas sob padronizada artificial crescente condições13. Entre essas duas abordagens são métodos rhizobox: eles usam imagens 2D de raízes crescendo no solo por longo período de tempo e tem taxa de transferência média21,22. Um desafio recente para imagem (2D) raiz é capturar também indicadores de funcionalidade de raiz, além da descrição da estrutura23.
No presente trabalho, apresentamos os protocolos experimentais para rhizobox crescido sistemas de raiz, usando (i) uma barata e simples feito por RGB de imagem (ii) um mais complexo NIR imagem instalação e imagem. Resultados de exemplo obtidos a partir destas duas configurações são mostrados e discutidos no contexto da planta fenotipagem e Plante a pesquisa fisiológica.

<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

Melhor entendimento da dinâmica de raiz da planta é essencial para melhorar a eficiência de utilização de recursos de sistemas agrícolas e aumentar a resistência das cultivares safra contra estresses ambientais. Um protocolo experimental é apresentado para RGB e imagem hiperespectral de sistemas da raiz. A abordagem utiliza rhizoboxes, onde plantas crescem no solo natural ao longo de um tempo maior para observar sistemas totalmente desenvolvidos de raiz. Configurações experimentais são exemplificadas pela avaliação rhizobox plantas sob estresse hídrico e estudar o papel das raízes. Uma configuração de imagem RGB é descrita para a quantificação de barata e rápida de desenvolvimento radicular ao longo do tempo. Imagens hiperespectrais melhora a segmentação de raiz do fundo solo comparado a limiarização de cor com base em RGB. A força especial de imagens hiperespectrais é a aquisição de quimiométricas informações sobre o sistema de raiz-solo para entendimento funcional. Isso é demonstrado com mapeamento conteúdo de água de alta resolução. Imagem espectral, no entanto, é mais complexa na aquisição de imagens, processamento e análise em comparação com a abordagem RGB. Uma combinação de ambos os métodos pode otimizar uma avaliação abrangente do sistema raiz. Exemplos de aplicações, integração de raiz e características na superfície são dadas para o contexto da pesquisa fisiológica fenotipagem e planta de planta. Reforço da imagem latente de raiz pode ser obtido através da otimização de qualidade de imagem RGB com melhor iluminação usando diferentes fontes de luz e, por extensão, dos métodos de análise de imagem para inferir sobre propriedades de zona raiz de dados espectrais.

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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