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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MyJove Corporation
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614140/ https://www.ncbi.nlm.nih.gov/pubmed/28809835 http://dx.doi.org/10.3791/56251 |
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author | Bodner, Gernot Alsalem, Mouhannad Nakhforoosh, Alireza Arnold, Thomas Leitner, Daniel |
author_facet | Bodner, Gernot Alsalem, Mouhannad Nakhforoosh, Alireza Arnold, Thomas Leitner, Daniel |
author_sort | Bodner, Gernot |
collection | PubMed |
description | Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data. |
format | Online Article Text |
id | pubmed-5614140 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MyJove Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-56141402017-10-10 RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols Bodner, Gernot Alsalem, Mouhannad Nakhforoosh, Alireza Arnold, Thomas Leitner, Daniel J Vis Exp Environmental Sciences Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data. MyJove Corporation 2017-08-08 /pmc/articles/PMC5614140/ /pubmed/28809835 http://dx.doi.org/10.3791/56251 Text en Copyright © 2017, Journal of Visualized Experiments http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visithttp://creativecommons.org/licenses/by-nc-nd/3.0/ |
spellingShingle | Environmental Sciences Bodner, Gernot Alsalem, Mouhannad Nakhforoosh, Alireza Arnold, Thomas Leitner, Daniel RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title | RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title_full | RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title_fullStr | RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title_full_unstemmed | RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title_short | RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols |
title_sort | rgb and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols |
topic | Environmental Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614140/ https://www.ncbi.nlm.nih.gov/pubmed/28809835 http://dx.doi.org/10.3791/56251 |
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