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

著者らは、プロジェクト数 P 25190-B16 (干ばつ抵抗性の根) 経由でオーストリア科学基金 FWF から資金を認めます。ハイパー スペクトル イメージング インフラストラクチャの確立は、連邦政府の低いオーストリア (土地 Niederösterreich) K3-F-282/001-2012 プロジェクトを介してによって財政上支えだった。AGRANA 研究から甜菜実験を受けたために追加の資金調達 &#38; イノベーション センター GmbH (ARIC)。著者は、実験と原稿の英文添削の中にテクニカル サポートのクレイグ ・ ジャクソンをありがちましょう。我々 はまた rhizobox マウントの建設のため RGB イメージング セットアップの確立に貢献したマーカス Freudhofmaier、ヨセフ Schodl を認めます。

<ol>
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著者が明らかに何もありません。

プロトコルは、ルート システムのイメージングを栽培された土壌の 2 つの相補的なアプローチを提供します。信頼性の高い実験結果を得るのための重要なステップは、観測窓でタイトなルート-土の接触を提供し、エアー ギャップを避けるためにフロント ガラスでも、均一な基板層を確保するためには rhizoboxes の充填です。比較的細かいふるわれた土を使用する主な理由は、この &#60; 2 mm: より大きな凝集体は、骨材間の空隙を持つ観測窓で高い表面形態。ルート ヒント脱水のリスクが高い、以外にもこれはまた水マッピング31のより複雑な画像処理技術を必要です。
プロトコルの変更従って rhizoboxes の向上と迅速な充填に焦点を当てます。現在充填時間ボックスあたり約 30 分です。さらにイメージングの側面より良い RGB 画像の照明の均一性を最適化するために変更からのため 2 枚のガラス窓で rhizoboxes の使用をテストします。ハードウェアをさらに拡張も33をイメージング rhizobox システム、容量と同様に、平面のバイオケミカル32の統合を検討するかもしれない。しかし、これは現在アップグレード以外は。
ソフトウェアの変更はヒューズの上部と下部の RBG 画像34にイメージの自動登録に焦点を当てます。ハイパー スペクトル イメージング高度な教師なし特徴抽出 Svm35テストより敏感なチャイルド ターゲット検出方法と同様、28をアプローチします。それによりハイパー スペクトル データは複数根と根圏土壌のプロパティの36の評価可能性のあるできます。さらにそれを目的とする (半) 建築形質 (分岐頻度、分岐の角度) および rhizobox ルートに基づく画像のルート システムの解析37形態を定量化するための修正バージョン (長さ、直径、表面) ソフトウェアを自動化します。
3 D イメージング技術と比較してプロトコルの主な制限は、表面の目に見える根および根圏プロパティに制限です。しかし21全体のルート システムの信頼性の高いプロキシに表示されているルートの特徴が実証されています。Rhizobox 技術は (洗濯) 総根系形質と表示の関係を検証するためにダイナミックな成長の終わりに伝統的な破壊的なサンプリングと簡単に結合されます。この関係は、21種の間で異なる場合があります、破壊的なサンプリングも別の作物の種を持つ、新しい表現型解析シリーズの目に見える特徴から信頼性の高い推論を確保するが推奨します。
ここで提示されたプロトコルの主な利点は、一時的解決の RGB 画像とルート機能 (例えば水通風管) ハイパースペクトル イメージング センサーからケモメトリック根と根圏データ経由での推論の潜在的なスループットが比較的高く、現実的な成長する条件 (土) の組み合わせです。それにより方法はハイスループット苗と非土根ながら部分的に以下の実験的複雑さと高度な 3 D 方法15と比べて高いスループット機能プロセス洞察深い表現、方法14をイメージングで推論の制限を克服します。
今後の実験では根系の発達と地盤構造物、窒素、炭素被覆作物種の表現型解析ルート特性に関してはマメ科植物と同様の機能に菌根菌の影響を検討するため、プロトコルが使用されますサイクリングします。

根植物のストレージなどのいくつかの重要な機能を提供する同化、土壌、および吸収における陸生植物の定着と水と栄養分の1の輸送。進化の観点から根の形成は土地の植物2の起源のための基本的な前提条件と見なされます。歴史的に、この重要な役割にもかかわらず根は生物学の研究だけ縁の位置を占めています。近年、しかしが高まって図 1からわかるように植物根系の科学的関心。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
図 1: ルート植物科学研究の関連性。 最後の十年にわたって科学雑誌にすべて公開された植物研究の割合としてルート数の関連の研究。Scopus キーワード「植物」と「植物とルート」を使用してからの結果を検索します。<a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">この図の拡大版を表示するのにはここをクリックしてください</a>。
2 つの主な理由は、根研究の最近の進歩の根底にあると仮定できます。まず、地上の植生は、地球変動3の結果としてより頻繁に環境ストレスにさらされます。農作物生産のコンテキストでグローバルに約農業の面積の 30% が水とリン4,5によって制限されることが推定されます。収穫量の応力低減効果は、天水農業生態系6の潜在的な生産性の低い 50% で推定される世界的に大幅な収量のギャップの主な理由です。低リソース可用性に加えてこれは、不適切なリソース利用効率、利用可能なリソース7を悪用する植物のすなわち十分な能力にも関連します。その他の生態系に悪影響を硝酸などモバイル リソースの損失でこの結果します。たとえば、現在の地球規模の窒素利用効率は 478と推定されます。良いリソース使用効率改善された管理のメソッドを介して、品種は両方のための高い重要性の環境の持続性に関しては同様に農業の出力の成長を持続します。このコンテキストの植物の根は、改良された作物の作付システム9,10の主要なターゲットと見なされます。
植物の根における最近の関心の 2 番目の重要な背景は、測定方法における技術の進歩です。Root メソッドは、2 つの主要な課題で長い制限されている:11、洗濯によって大抵定量化, 分離する有した土壌で育つ植物からの根の測定のために起こし根の建築配置。発掘を使用してその場でルート観察植物説明12土壌における根の自然な場所を節約の方法が使用されています。まだ彼らは非常に時間がかかる、従って比較構造機能ルート システム分析のスループットの要件を満たしていません。一方、ルート アーキテクチャ測定用高スループット方法ほとんど行われた実生植物13人工培植物の自然な成長環境への外挿が疑わしい14。
根研究の最近のブームは、方法15のイメージングの進歩に密接に結びついた。ルート研究のアプローチをイメージングすることができます約 3 つのタイプに分類します。最初16CT や MRI などの高解像度 3 D 方法があります。これらのメソッドは、干ばつによる木部塞栓症17など、土壌と植物の根の相互作用過程の研究に最も適しています。通常、彼らは詳細な観測ができる比較的小さいサンプルに適用されます。異なるサイズの鍋および細根画像診断用 CT と MRI の比較は、18で提供されます。第二に、高スループットのイメージング方法19,20があります。これらのメソッドは主に基づいて一般的な RGB 画像人工メディア (ゲル、発芽紙) の成長の根の高コントラストがルーツと背景との間の比較的単純な郭清を可能します。彼らは標準化された人工成長条件13の下で別の作物品種の苗ルートの特徴の高スループット比較に適しています。Rhizobox メソッドは、これらの 2 つのアプローチの間に: 中スループット21,22を長い期間にわたって土壌で育つ根の 2次元イメージングを使用しています。(2 D) ルート イメージングへの最近の課題は、また構造23の説明に加えて、ルート機能の指標をキャプチャすることです。
本論文では rhizobox (i) 安くて簡単なカスタム RGB 画像設定と (ii) より複雑な近赤外イメージング セットアップを使用して根系の成長をイメージングの実験的プロトコルを提案する.これらの 2 つの設定から得られる結果の例は表示され、植物の表現型解析と植物生理学的研究のコンテキストで説明.

<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

植物根のダイナミクスの理解は、農業のシステム リソースの使用効率を向上させ、環境ストレスと作物品種の抵抗性に不可欠です。RGB と根系のハイパー スペクトル イメージングの実験的プロトコルが表示されます。アプローチは、植物が完全に発達した根系を観察する長い時間をかけて自然な土で育つ rhizoboxes を使用しています。水ストレス下の rhizobox 植物を評価し、根の役割を研究するため実験的設定が挙げられます。時間をかけて根の安くて簡単な定量化のため RGB イメージング セットアップを説明します。ハイパースペクトル イメージング センサーは RGB 色の閾値と比較して土壌の背景からルート セグメンテーションを向上させます。ハイパースペクトル イメージング センサーの特定の強さはケモメトリック根土壌システム機能の理解のために情報の取得です。これを高解像度水コンテンツ マッピングを示します。分光イメージングはしかし画像の取得・処理・解析の RGB のアプローチに比べて複雑です。両方のメソッドの組み合わせは、ルート システムの包括的な評価を最適化できます。植物の表現型解析と植物の生理学的研究のコンテキストの根と地上部の形質を統合するアプリケーションの例のとおりです。別の光源を使用してより良い照明と RGB 画像の品質を最適化することによって、スペクトル データからルート ゾーンのプロパティを推測する画像解析法の延長によってルート イメージングのさらなる改善が得られます。

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 とスペクトルのルート画像植物表現型解析および生理学的研究: 実験概要およびイメージ投射プロトコル

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