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

作者承认奥地利的科学基金判断力，通过项目数 P 25190 B16 （抗旱性的根源） 的资助。建立高光谱成像的基础设施是由联邦政府的低奥地利 (土地 Niederösterreich) 通过项目 K3-F-282/001-2012年财政支持。额外经费糖用甜菜实验未收到安娜研究 &#38; 创新中心 GmbH (ARIC)。作者感谢克雷格 · 杰克逊在实验和英语校正的手稿期间给予技术支持。我们还感谢 Markus Freudhofmaier 建立的 RGB 图像设置作出贡献和约瑟夫朔德尔根安装施工。

<ol>
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作者没有透露。

议定书 》 为土壤种植根系统成像提供两种互补的方法。可靠的实验结果的关键步骤填补已确保在前面的玻璃为提供紧根-土接触在观察窗和避免空气间隙甚至和均匀的基材层的 rhizoboxes。这是主要的原因，使用较细筛的土的 &#60; 2 毫米： 大团聚体导致更高的表面形貌观察窗口与骨料之间的空隙。除了根尖脱水风险较高，这也需要更复杂的图像处理技术对水映射31。
修改议定书 》 因此专注于改进和快速充填的 rhizoboxes。目前充模时间是每盒约 30 分钟。此外 rhizoboxes 与两个玻璃窗口用于成像从双方和修改，以优化为更好的 RGB 图像的照明均匀性进行了测试。进一步的硬件扩展也可以考虑平面 optodes32以及电容成像33到根系统的集成。然而，这是超越当前的升级活动。
软件修改专注于图像自动配准融合顶部和底部 RBG 图像34。为高光谱成像的先进的无监督的特征提取方法，28 ，以及更为敏感的监督的目标检测方法如支持向量机35进行测试。从而高光谱数据可能允许多个土壤，根际和根属性36评定。此外，它旨在开发 （半） 自动化软件根根基于图像修改后的版本的根系统分析仪37量化形态 （长度、 直径、 表面） 以及建筑特质 （分支频率，分枝角度）。
议定书 》 相比，3D 成像方法的主要限制是对表面可见的根和根际土壤性质的限制。然而已表明，可见根系性状是整个根系21可靠的代理。根技术很容易结合传统破坏性采样 （洗） 在结束了生长动态成像以验证的可见与总根系性状的关系。因为这种关系可能有所不同物种21，破坏取样被建议，以确保可靠推理从可见的任何新的分型系列，与不同的作物物种性状。
这里介绍的议定书 》 的关键优势是现实生长条件 （土壤）、 时空分辨的 RGB 成像和推理根功能 （如吸水） 通过化学计量学根和根际数据从高光谱成像潜在吞吐量相对较高的组合。从而方法克服了推理限制高吞吐量幼苗期和非土根成像方法14，虽然与少实验系统的复杂性和更高的吞吐量，相比先进的 3D 方法15部分允许深分型洞察功能的过程。
在即将实验议定书 》 将用于研究菌根对根系发育和功能的豆类以及对于覆盖作物物种相对于土壤结构、 氮、 碳分型根系特性的影响循环。

根为植物如存储的提供几个基本职能同化，锚固的陆生植物在土壤和吸收和运输的水分和养分1。从进化的角度来看，根轴的形成被认为是陆地植物2原产地的基本前提。尽管这个重要的角色，历史上根有占领只在生物学研究中的边缘地位。在较近的时候，然而，那里科学兴趣在增加植物根系统如图 1所示。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
图 1： 根在植物科学研究的关联性。 根的数目过去数十年在 SCI 期刊中的所有已发布的植物研究的百分比相关研究。搜索结果从 Scopus 使用关键字"植物"和"植物和根"。<a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">请点击这里查看此图的大版本。</a>
两个主要原因可以假设根研究进展的基础。第一，陆地植被被暴露在更频繁的环境应力，由于全球变化3。在农业作物生产方面估计，全球大约 30%的农业地区都受到水和磷4，5。作物产量的减少应激是生产力的全球估计低 50%的潜在，为旱作农业生态系统6的重大收益率差距的主要原因。除了可用资源不足，这也被有关差资源的利用效率，即没有足够的能力，一种植物利用可用资源7。这导致损失的移动的资源，如能对其他生态系统产生负面影响的硝酸盐。例如当前全球氮利用效率估计 478。更好的资源利用效率，通过改进的管理方法，故其品种为两个高重要性的持续增长，农业的产出以及环境的可持续性。在这种背景下植物根被认为是改良的作物和种植系统9，10的关键目标。
第二个重要的背景，对于植物根系的最近的兴趣是科技进步的测量方法。根方法长已经限制了两个关键的挑战： 从他们不得不被隔离，以便量化，主要由洗11的土壤中生长的植物根测量从而扰乱建筑布局的根轴。原位根观察使用开挖方法，从而节约根在土壤中的自然位置已用于植物描述12。仍他们非常耗费时间，因此不符合比较结构功能的根系统分析的吞吐量要求。另一方面根建筑测量高通量方法主要是进行在人工培养基上，苗植物13推到植物的自然生长环境哪里可疑14。
根研究最近的繁荣是紧密相连的进展成像方法15。影像学方法在根研究可大致分为三种类型。首先是高分辨率 CT 和 MRI163D 方法。这些方法是最适合研究植物根系与土壤的相互作用过程，如干旱诱导木质部栓塞17。通常他们都被应用于相对较小的样本，他们允许的详细观测资料。CT 和 mri 表现为不同大小的锅和细根成像比较被提供18。第二，有高吞吐量成像方法19，20。这些方法是主要基于共同 2D RGB 图像进行根的生长在人工培养基 （凝胶，萌发纸张） 上高对比度让比较简单剖析根源和背景之间的所在。他们是适合高吞吐量比较标准化人工生长条件13下的不同作物基因型幼苗根系性状。在这两种方法之间是根方法： 他们使用二维成像的根的土壤中生长在较长时间段内，有中等吞吐量21，22。(2D) 根成像新挑战是捕捉也描述结构23根功能指标。
在本论文中我们提出成像根生长根系统使用 （i) 便宜和简单定制 RGB 成像和 （ii) 更复杂近红外成像安装实验规程。从这两个设置的示例结果显示并在植物表型和植物生理研究的范围内讨论。

<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

植物根动态更好地理解至关重要，提高资源利用效率的农业系统，增加对环境胁迫的作物品种的抗病性。RGB 和高光谱成像的根系统，提出了一种实验性的协议。该方法使用 rhizoboxes 植物在自然土壤中生长在很长的时间来观察充分发达的根系。实验设置并举例说明了评估水分胁迫下的根植物和研究根的作用。RGB 图像设置根发展随着时间的推移的廉价和快速的定量描述。高光谱成像技术可以提高根从土壤背景相比 RGB 彩色图像阈值分割。高光谱成像的特定强度是根-土系统的功能理解化学计量学信息的获取。这说明了高分辨率水内容映射。光谱成像技术不过是在图像采集、 处理和分析的 RGB 方法相比更加复杂。结合这两种方法可以优化根系统全面的评估。应用集成根和地上部性状实例的上下文植物表型和植物生理研究。通过优化 RGB 图像质量和更好的照明，使用不同的光源和图像分析方法来推断对根区域属性从光谱数据的推广，可以获得根成像的进一步改进。

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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