저자는 프로젝트 번호 P 25190 B16 (가뭄 저항의 뿌리)를 통해 오스트리아 과학 기금 FWF에서 자금을 인정 합니다. Hyperspectral 이미징 인프라의 설립은 연방 정부의 낮은 오스트리아 (토지 러시아) K3-F-282/001-2012 프로젝트를 통해에 의해 재정적으로 지원 되었다. AGRANA 연구에서 받은 사탕 실험에 대 한 추가 자금 &#38; 혁신 센터 GmbH (ARIC). 저자는 실험과 원고의 영어 교정 기술 지원에 대 한 크레이그 잭슨을 감사합니다. 우리 또한 인정 설립 RGB 영상 설치에 공헌한 Markus Freudhofmaier 조세 프 Schodl rhizobox 설치의 건설에 대 한 합니다.

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저자는 공개 없다.

프로토콜 루트 시스템 이미징 성장 하는 토양에 대 한 두 개의 상호 보완적인 접근을 제공 합니다. 신뢰할 수 있는 실험 결과 대 한 중요 한 단계는 관측 창에 꽉 루트-토양 접촉을 제공 하 고 공기 간격을 방지 하는 전면 유리에도 동종 기판 레이어를 보장 하는 rhizoboxes의 작성 이다. 이것의 비교적 좋은 체질된 흙을 사용 하는 주요 이유는 &#60; 2: 골 재 사이의 공 극으로 관찰 창에서 더 높은 표면 형태에 더 큰 집계 결과. 루트 팁 탈수의 위험이 높은, 외이 물 매핑31에 대 한 더 복잡 한 이미지 처리 기법을 필요합니다.
프로토콜의 수정 따라서 rhizoboxes의 향상 및 빠른 작성에 초점을. 현재 시간을 작성 하는 것은 상자 당 약 30 분입니다. 또한 측 및 더 나은 RGB 이미지에 대 한 조명 동질성을 최적화 하기 위해 수정에서 영상에 대 한 두 개의 유리창와 rhizoboxes의 사용은 테스트. 추가 하드웨어 확장 또한 평면 optodes32 rhizobox 시스템으로33 이미징 커패시턴스의 통합을 좋습니다. 그러나 이것이 현재 업그레이드 활동 넘어입니다.
소프트웨어 수정 퓨즈 상단 및 하단 RBG 이미지34자동 이미지 등록에 초점. Hyperspectral에 대 한 이미징 고급 자동된 기능 추출 SVMs35 테스트와 같은 더 민감한 감독된 대상 탐지 방법으로28 접근 한다. 그로 인하여 hyperspectral 데이터는 잠재적으로 여러 토양, rhizosphere 및 루트 속성36의 평가 대 한 허용. 개발 (세미)는 것 또한 건축 특성 (분기 주파수, 각 분기) 뿐만 아니라 rhizobox 루트에 따라 이미지 루트 시스템 분석기37 형태학 계량의 수정된 된 버전 (길이, 직경, 표면)에 대 한 소프트웨어 automatized.
3D 이미징 접근에 비해 프로토콜의 주요 제한은 표면 표시 루트 및 rhizosphere 속성 제한입니다. 그러나 그것은 보이는 루트 특성 전체 루트 시스템21에 대 한 신뢰할 수 있는 프록시는 증명 되었습니다. Rhizobox 기술은 전통적인 파괴적인 샘플링 (세척) 동적 성장 전체 루트 시스템 특성 대 표시의 관계를 확인 하려면 이미징의 끝에와 쉽게 결합 된다. 이 관계는 종21중 다 수, 파괴적인 샘플링 다른 작물 종으로 어떤 새로운 형질 시리즈에 대 한 표시 특성에서 신뢰할 수 있는 유추 되도록 것이 좋습니다.
여기에 제시 된 프로토콜의 주요 장점은 현실적인 성장 조건 (토양), 일시적으로 해결 RGB 이미지와 루트 기능 (예: 물 통풍 관) hyperspectral 화상 진 찰에서 chemometric 및 rhizosphere 데이터를 통해 유추에 대 한 상대적으로 높은 잠재적인 처리량의 조합입니다. 그로 인하여 방법 유추 제한 높은 처리량 경종 및 비-토양 루트 방법14, 그것은 부분적으로 덜 실험적인 복잡성과 고급 3D 방법15에 비해 높은 처리량으로 업무 프로세스에 대 한 깊은 형질 통찰력을 허용 하는 동안 이미지를 극복 한다.
곧 실험 프로토콜 mycorrhiza 루트 시스템 개발 및 토양 구조, 질소 및 탄소 관련 된 덮개 작물 종의 형질 루트 특성에 관해서는 뿐만 아니라 콩의 기능에의 효과 연구 하는 데 사용 됩니다 사이클링.

뿌리의 저장 등의 식물에 대 한 몇 가지 필수 기능을 제공 동화 한다, 토양, 및 통풍 관 지구 식물의 앵커리지와 물과 영양소1의 전송. 진화 관점에서의 루트 축 형성 땅 식물2의 기원에 대 한 기본 전제 조건으로 간주 됩니다. 이 중요 한 역할에도 불구 하 고 역사적으로 뿌리 생물학 연구에만 한계 위치를 점령 했습니다. 그러나 더 최근에,, 거기는 증가 그림1에서 입증 공장 루트 시스템에 과학적인 관심사.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56251/56251fig1.jpg"> 
그림 1: 루트 식물 과학에 있는 연구의 관련성. 루트의 수는 지난 년간 SCI 저널에 모든 게시 된 식물 연구의 연구를 관련. Scopus 키워드 "공장" 및 "공장 및 루트"를 사용 하 여에서 결과 검색 합니다. <a href="//ecsource.jove.com/files/ftp_upload/56251/56251fig1large.jpg" target="_blank">이 그림의 더 큰 버전을 보려면 여기를 클릭 하십시오.</a>
두 가지 주요 이유는 루트 연구의 최근 발전 기초를 가설 수 있습니다. 첫째, 지상파 식물 글로벌 변경3결과로 더 자주 환경 스트레스에 노출 됩니다. 농업 작물 생산의 맥락에서 그것은 세계적으로 약 30%의 농업 지역 물과 인4,5에 의해 제한 됩니다 추정. 수확량의 스트레스 감소는 세계적으로 낮은 50 %rainfed 농업 생태계6에 대 한 잠재적인 생산성의 추정 되어 상당한 수익률 격차에 대 한 주요 이유. 낮은 리소스 가용성, 외이 가난한 리소스 사용 효율, 즉 부족 한 수 식물의 사용 가능한 리소스7악용 관련이 있습니다. 이 다른 생태계에 부정적인 영향을 미칠 수 있는 질산염 등 모바일 자원의 손실에 결과. 예를 들어 현재 글로벌 질소 이용 효율 478추정 됩니다. 더 나은 자원 사용 효율성 향상 된 관리 방법 및 재배 이므로 모두 높은 중요성의 성장 환경 지속 가능성 뿐만 아니라 농업 출력의 지속. 이 컨텍스트 식물에서 뿌리 향상 된 작물 및 자르기 시스템9,10에 대 한 주요 대상으로 간주 됩니다.
식물 뿌리에 최근 관심사에 대 한 두 번째 중요 한 배경 측정 방법에 기술적 진보 이다. 루트 방법을 두 가지 주요 과제에 의해 제한 오래 있다: 그들은 주로11, 세척 하 여 정량화에 대 한 격리 해야 했다 토양에서 성장 하는 식물에서 뿌리의 측정에 대 한 그로 인하여 방해 루트 축의 건축 배열. 발굴을 사용 하 여 제자리에 루트 관찰 함으로써 토양에서 뿌리의 자연 스러운 위치를 보존 하는 방법 식물 설명12사용 되 고 있다. 아직도 그들은 매우 시간이 걸리는 고 따라서 비교 구조 기능 루트 시스템 분석의 처리량 요구 사항을 충족 하지 않습니다. 다른 한편으로 높은 처리량 방법 루트 건축 측정 했다 주로 수행 인공 미디어에 묘 종 식물13 식물의 자연 성장과 환경 추정 의심14가.
루트 연구의 최근 붐 이미징 방법15에 사전에 긴밀 하 게 연결 된다. 접근 루트 연구에서를 이미징 세 가지 유형으로 대략 그룹화 될 수 있습니다. 우선 CT 및 MRI16같은 고해상도 3D 방법이 있다. 이러한 메서드는 가뭄 유도 xylem 폐색전증17와 같은 흙과 식물 뿌리의 상호 작용 과정 연구에 가장 적합 한. 일반적으로 그들은 그들은 상세한 관측을 허용 하는 비교적 작은 샘플에 적용 됩니다. 다른 크기의 냄비와 잘 루트 이미징에 대 한 CT와 MRI의 비교는18에 제공 됩니다. 둘째, 높은 처리량 이미징 방법19,20있다. 이러한 메서드는 대부분에 따라 일반적인 2D RGB 영상 인공 미디어 (젤, 발 아 종이)에 성장 하는 뿌리의 고대비 뿌리와 배경 사이의 비교적 간단한 해 부를 허용 하는 곳. 그들은 다른 작물 genotypes 표준화 된 인공 성장 조건13에서 종묘 루트 특성 중 높은 처리량 비교를 위해 적합 하다. Rhizobox 메서드는이 두 가지 방법 사이: 그들은 더 긴 기간 동안 토양에 성장 하는 뿌리의 2D 이미지를 사용 하 고 중간 처리량21,22. (2D) 루트 이미징에 대 한 최근 도전 캡처 또한 구조23의 설명 외에도 루트 기능 지표입니다.
현재 신문에서 우리는 rhizobox 성장 (i)는 저렴 하 고 간단한 주문 품 RGB 이미징 설치 및 (ii)는 더 복잡 한 적외선 이미징 설치 프로그램을 사용 하 여 루트 시스템 이미징을 위한 실험 프로토콜 제시. 이러한 두 설정에서 예제 결과 표시 되며 식물 형질 및 식물 생리 연구의 맥락에서 논의.

<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

식물 루트 역학의 더 나은 이해 농업 시스템의 리소스 사용 효율을 개선 하 고 작물 재배 환경 스트레스에 대 한 저항 증가 필수적 이다. 실험 프로토콜은 RGB 및 루트 시스템의 hyperspectral 화상 진 찰에 대 한 제공 됩니다. 접근은 식물 자연 토양에서 완전 개발된 루트 시스템을 관찰 하는 더 긴 시간을 통해 성장 rhizoboxes를 사용 합니다. 실험 설정 됩니다 rhizobox 식물 물 스트레스를 평가 하 고 뿌리의 역할을 공부 하 고 궁 행. RGB 이미지 설치 시간이 지남에 루트 개발의 저렴 하 고 빠른 정량화에 대 한 설명 합니다. Hyperspectral 영상 RGB 색상 기반 임계 처리에 비해 토양 배경에서 루트 분할을 향상 시킵니다. Hyperspectral 화상의 특정 힘은 기능 이해에 대 한 루트 토양 시스템에 chemometric 정보 수집 이다. 이 고해상도 물 콘텐츠 매핑 보여 줍니다. 그러나 스펙트럼 이미징은 이미지 수집, 처리 및 분석 RGB 방식에 비해 더 복잡 합니다. 두 방법의 조합에는 루트 시스템의 포괄적인 평가 최적화할 수 있습니다. 루트 및 지상 특성을 통합 하는 응용 프로그램 예제는 식물 형질 및 식물 생리 연구의 맥락에 대 한 제공 됩니다. 루트 영상의 추가 개선 및 확장 스펙트럼 데이터에서 루트 영역 속성에 유추 이미지 분석 방법의 다른 광원을 사용 하 여 더 나은 조명 RGB 이미지 품질을 최적화 하 여 얻을 수 있습니다.

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 및 식물 형질 및 생리 적 연구에 대 한 스펙트럼 루트 이미징: 실험적인 체제 및 프로토콜을 이미징

실험 프로토콜은 RGB와 hyperspectral 화상 식물 루트 시스템을 성장 하는 토양의 평가 대 한 제공 됩니다. RGB 이미지 시간 시리즈 hyperspectral에서 chemometric 정보 검색의 조합 공장 루트 역학에 대 한 통찰력을 최적화 합니다.

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16.1K 조회수.  University of Natural Resources and Life Sciences. 실험 프로토콜은 RGB와 hyperspectral 화상 식물 루트 시스템을 성장 하는 토양의 평가 대 한 제공 됩니다. RGB 이미지 시간 시리즈 hyperspectral에서 chemometric 정보 검색의 조합 공장 루트 역학에 대 한 통찰력을 최적화 합니다.

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

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.

3 차원 루트 시스템에 대한 통계를 추출 : 볼륨 및 표면 분석에-토양 X 선 컴퓨터 단층 촬영 데이터에서

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

연구

환경과학

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.

루트 성장 및 지역화 된 양분에 응답을 시각화 하는 최적화 된 Rhizobox 프로토콜

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.

곡물 작물 귀 필드에서 조건 방들은 RGB 이미지를 사용 하 여

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.

식물-환경 상호 작용의 실시간 생리적 페노티핑을 위한 텔레메트릭, 중력 플랫폼

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 ...

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with particular attention to paintings. Most modern museums put at the disposal of their research groups portable equipment that, together with the intrinsic non-invasiveness of RS and FORS, makes possible the in situ collection of reflectance spectra from the surface of artefacts. The comparison, performed by experts in pigments and painting materials, of the experimental data with databases of reference spectra drives the characterization of the palettes and of the techniques used by the artists. However, this approach requires specific skills and it is time consuming especially if the number of the spectra to be investigated becomes large as is the case of Hyperspectral Reflectance Imaging (HRI) datasets. The HRI experimental setups are multi-dimensional cameras that associate the spectral information, given by the reflectance spectra, with the spatial localization of the spectra over the painted surface. The resulting datasets are 3D-cubes (called hypercubes or data-cubes) where the first two dimensions locate the spectrum over the painting and the third is the spectrum itself (i.e., the reflectance of that point of the painted surface versus the wavelength in the operative range of the detector). The capability of the detector to simultaneously collect a great number of spectra (typically much more than 10,000 for each hypercube) makes the HRI datasets large reservoirs of information and justifies the need for the development of robust and, possibly, automated protocols to analyze the data. After the description of the procedure designed for the data acquisition, we present an analysis method that systematically exploits the potential of the hypercubes. Based on Spectral Angle Mapper (SAM) and on the manipulation of the collected spectra, the algorithm handles and analyzes thousands of spectra while at the same time it supports&#160;the user to unveil the features of the samples under investigation. The power of the approach is illustrated by applying it to Quarto Stato, the iconic masterpiece by Giuseppe Pellizza da Volpedo, held in the Museo del Novecento in Milan (Italy).

Hyperspectral Reflectance Imaging hypercubes include remarkable information into a large amount of data. Therefore, the request for automated protocols to manage and study the datasets is widely justified. The combination of Spectral Angle Mapper, data manipulation, and a user-adjustable analysis method constitutes a key-turn for exploring the experimental results.

팔레트와 화가의 기술을 조사하기 위해 하이퍼 스펙트럼 반사 영상 적용

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with ...

화학

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.

잎 반사 및 엽록소 형광 분석의 동시 측정을 통해 광합성 거동 평가

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

생체공학

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.

애기 장대 루트 Gravitropic 응답의 고해상도 시간 경과 이미지를 수집하는 평판 스캐너를 사용하여

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

생물학

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.

플랜트 루트 시스템 아키텍처 특성을 매핑하기 위한 간단한 프로토콜

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

기후 관련 스트레스에 따른 감자 복원력 분석

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.

저자 스포트라이트: PlantScreen 로봇 플랫폼을 사용하여 비생물적 스트레스에 대한 식물의 반응 규명

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.

감자의 단일 및 결합 스트레스에 대한 형태학적 및 생리학적 반응을 결정하기 위한 고처리량 이미지 기반 표현형 분석

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

RGB 및 식물 형질 및 생리 적 연구에 대 한 스펙트럼 루트 이미징: 실험적인 체제 및 프로토콜을 이미징

요약

더 많은 비디오 탐색

3 차원 루트 시스템에 대한 통계를 추출 : 볼륨 및 표면 분석에-토양 X 선 컴퓨터 단층 촬영 데이터에서

루트 성장 및 지역화 된 양분에 응답을 시각화 하는 최적화 된 Rhizobox 프로토콜

곡물 작물 귀 필드에서 조건 방들은 RGB 이미지를 사용 하 여

식물-환경 상호 작용의 실시간 생리적 페노티핑을 위한 텔레메트릭, 중력 플랫폼

팔레트와 화가의 기술을 조사하기 위해 하이퍼 스펙트럼 반사 영상 적용

잎 반사 및 엽록소 형광 분석의 동시 측정을 통해 광합성 거동 평가

애기 장대 루트 Gravitropic 응답의 고해상도 시간 경과 이미지를 수집하는 평판 스캐너를 사용하여

플랜트 루트 시스템 아키텍처 특성을 매핑하기 위한 간단한 프로토콜

감자의 단일 및 결합 스트레스에 대한 형태학적 및 생리학적 반응을 결정하기 위한 고처리량 이미지 기반 표현형 분석

감자의 단일 및 결합 스트레스에 대한 형태학적 및 생리학적 반응을 결정하기 위한 고처리량 이미지 기반 표현형 분석