Name: rgb and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols
Uploaded: 2017-08-08T15:00:00.000+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&amp;uuml;ro f&amp;uuml;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

RGB 和光谱根成像植物表型和生理研究: 实验装置和成像协议

为土壤种植植物根系与 RGB 和高光谱成像评价提出了一种实验性的协议。RGB 图像时间系列与化学计量学信息从高光谱扫描组合优化植物根动力学的见解。

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

rgb-spectral-root-imaging-for-plant-phenotyping-physiological

Universidad de Cartagena UDEC

Research

JoVE Journal

Environment

16.1K 次观看.  University of Natural Resources and Life Sciences. 为土壤种植植物根系与 RGB 和高光谱成像评价提出了一种实验性的协议。RGB 图像时间系列与化学计量学信息从高光谱扫描组合优化植物根动力学的见解。

视频: RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Plant biomass offers a renewable resource for multiple products, including fuel, feed, food, and a variety of materials. In this paper we investigate the properties of tobacco tree (Nicotiana glauca) and poplar as suitable sources for a biorefinery pipeline.

成绩单及代谢物谱烟草树和杨树的评价作为原料的生物基工业

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the ...

科研

环境科学

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

从在土X射线计算机断层扫描数据量和表面分析:提取指标三维根系

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

一种优化的 Rhizobox 协议, 用于可视化根系生长和对局部营养素的响应

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

利用泽尼塔尔 rgb 图像进行田间作物耳计数

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&ndash;Environment Interactions

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating a knowledge gap. To meet the challenge of improving crops to feed the growing global population, this gap must be bridged.
Physiological traits are considered key functional traits in the context of responsiveness or sensitivity to environmental conditions. Many recently introduced high-throughput (HTP) phenotyping techniques are based on remote sensing or imaging and are capable of directly measuring morphological traits, but measure physiological parameters mainly indirectly.
This paper describes a method for direct physiological phenotyping that has several advantages for the functional phenotyping of plant&#8211;environment interactions. It helps users overcome the many challenges encountered in the use of load-cell gravimetric systems and pot experiments. The suggested techniques will enable users to distinguish between soil weight, plant weight and soil water content, providing a method for the continuous and simultaneous measurement of dynamic soil, plant and atmosphere conditions, alongside the measurement of key physiological traits. This method allows researchers to closely mimic field stress scenarios while taking into consideration the environment&#8217;s effects on the plants&#8217; physiology. This method also minimizes pot effects, which are one of the major problems in pre-field phenotyping. It includes a feed-back fertigation system that enables a truly randomized experimental design at a field-like plant density. This system detects the soil-water-content limiting threshold (&#952;) and allows for the translation of data into knowledge through the use of a real-time analytic tool and an online statistical resource. This method for the rapid and direct measurement of the physiological responses of multiple plants to a dynamic environment has great potential for use in screening for beneficial traits associated with responses to abiotic stress, in the context of pre-field breeding and crop improvement.

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant&#8211;Environment Interactions

This high-throughput, telemetric, whole-plant water relations gravimetric phenotyping method enables direct and simultaneous real-time measurements, as well as the analysis of multiple yield-related physiological traits involved in dynamic plant&#8211;environment interactions.

植物与环境相互作用实时生理表型的遥测重力平台

Food security for the growing global population is a major concern. The data provided by genomic tools far exceeds the supply of phenotypic data, creating ...

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many parameters that efficiently measure photosynthesis. Leaf reflectance analysis provides several vegetation indices in ecology and agriculture, including the photochemical reflectance index (PRI), which can be used as an indicator of thermal energy dissipation during photosynthesis because it correlates with non-photochemical quenching (NPQ). However, since NPQ is a composite parameter, its validation is required to understand the nature of the PRI parameter. To obtain physiological evidence for evaluation of the PRI parameter, we simultaneously measured chlorophyll fluorescence and leaf reflectance in xanthophyll cycle defective mutant (npq1) and wild-type Arabidopsis plants. Additionally, the qZ parameter, which likely reflects the xanthophyll cycle, was extracted from the results of chlorophyll fluorescence analysis by monitoring relaxation kinetics of NPQ after switching the light off. These simultaneous measurements were carried out using a pulse-amplitude modulation (PAM) chlorophyll fluorometer and a spectral radiometer. The fiber probes from both instruments were positioned close to each other to detect signals from the same leaf position. An external light source was used to activate photosynthesis, and the measuring lights and saturated light were provided from the PAM instrument. This experimental system enabled us to monitor light-dependent PRI in the intact plant and revealed that light-dependent changes in PRI differ significantly between the wild type and npq1 mutant. Furthermore, PRI was strongly correlated with qZ, meaning that qZ reflects the xanthophyll cycle. Together, these measurements demonstrated that simultaneous measurement of leaf reflectance and chlorophyll fluorescence is a valid approach for parameter evaluation.

We describe a new technical approach to study photosynthetic responses in higher plants involving simultaneous measurements of chlorophyll a fluorescence and leaf reflectance using a PAM and a spectral radiometer for the detection of signals from the same leaf area in Arabidopsis.

通过叶反射和叶绿素荧光分析同步测量的光合行为评价

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many ...

生物工程

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories can face is the development and attainment of these methods. Observation of phenotypes in a process of interest is a typical objective of research labs studying gene function and this is often achieved through image capture. A particular process that is amenable to observation using imaging approaches is the corrective growth of a seedling root that has been displaced from alignment with the gravity vector. Imaging platforms used to measure the root gravitropic response can be expensive, relatively low in throughput, and/or labor intensive. These issues have been addressed by developing a high-throughput image capture method using inexpensive, yet high-resolution, flatbed scanners. Using this method, images can be captured every few minutes at 4,800 dpi. The current setup enables collection of 216 individual responses per day. The image data collected is of ample quality for image analysis applications.

This protocol describes a process for rapid collection of images of Arabidopsis seedlings responding to a gravity stimulus using commercially-available flatbed scanners. The method allows for inexpensive, high-volume capture of high-resolution images amenable for downstream analysis algorithms.

使用平板扫描仪，以收集拟南芥根向地反应的高分辨率时间失效图片

Research efforts in biology increasingly require use of methodologies that enable high-volume collection of high-resolution data. A challenge laboratories ...

生物学

A Simple Protocol for Mapping the Plant Root System Architecture Traits

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar tolerance to environmental challenges. An experimental protocol is presented for setting up the hydroponic system, plantlet growth, RSA spreading, and imaging. The approach used a magenta box-based hydroponic system containing polypropylene mesh supported by polycarbonate wedges. Experimental settings are exemplified by assessing the RSA of the plantlets under varying nutrient (phosphate [Pi]) supply. The system was established to examine the RSA of Arabidopsis, but it is readily adaptable to study other plants like Medicago sativa (Alfalfa). Arabidopsis thaliana (Col-0) plantlets are used in this investigation as an example to understand the plant RSA. Seeds are surface sterilized by treating ethanol and diluted commercial bleach, and kept at 4 &#176;C for stratification. The seeds are germinated and grown on a liquid half-MS medium on a polypropylene mesh supported by polycarbonate wedges. The plantlets are grown under standard growth conditions for the desired number days, gently picked out from the mesh, and submersed in water-containing agar plates. Each root system of the plantlets is spread gently on the water-filled plate with the help of a round art brush. These Petri plates are photographed or scanned at high resolution to document the RSA traits. The root traits, such as primary root, lateral roots, and branching zone, are measured using the freely available ImageJ software. This study provides techniques for measuring plant root characteristics in controlled environmental settings. We discuss how to (1) grow the plantlets, and collect and spread root samples, (2) obtain pictures of spread RSA samples, (3) capture the images, and (4) use image analysis software to quantify root attributes. The advantage of the present method is the versatile, easy, and efficient measurement of the RSA traits.

We use simple laboratory tools to examine the root system architecture (RSA) of Arabidopsis and Medicago. The plantlets are grown hydroponically over mesh and spread using an art brush to reveal the RSA. Images are taken using scanning or a high-resolution camera, then analyzed with ImageJ to map traits.

用于映射植物根系架构特征的简单协议

Comprehensive knowledge of plant root system architecture (RSA) development is critical for improving nutrient use efficiency and increasing crop cultivar ...

气候相关胁迫下的马铃薯恢复力分析

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to reveal&#160;the dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

作者聚焦:使用 PlantScreen 机器人平台揭示植物对非生物胁迫的响应

We designed an image-based phenotyping protocol to determine the morphological and physiological responses to single and combined heat, drought, and waterlogging treatments. This approach enabled the identification of early, late, and recovery responses at a whole plant level, particularly above-ground parts, and highlighted the necessity of using multiple imaging sensors.

基于图像的高通量表型分析用于确定马铃薯对单一胁迫和联合胁迫的形态和生理响应

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions ...

RGB 和光谱根成像植物表型和生理研究: 实验装置和成像协议

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植物与环境相互作用实时生理表型的遥测重力平台

通过叶反射和叶绿素荧光分析同步测量的光合行为评价

使用平板扫描仪，以收集拟南芥根向地反应的高分辨率时间失效图片

用于映射植物根系架构特征的简单协议

基于图像的高通量表型分析用于确定马铃薯对单一胁迫和联合胁迫的形态和生理响应

基于图像的高通量表型分析用于确定马铃薯对单一胁迫和联合胁迫的形态和生理响应