Hyperspectral imaging: a novel approach for plant root phenotyping

Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plan...
Ausführliche Beschreibung

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Autor*in:	Gernot Bodner [verfasserIn] Alireza Nakhforoosh [verfasserIn] Thomas Arnold [verfasserIn] Daniel Leitner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum

Übergeordnetes Werk:	In: Plant Methods - BMC, 2005, 14(2018), 1, Seite 17
Übergeordnetes Werk:	volume:14 ; year:2018 ; number:1 ; pages:17

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13007-018-0352-1

Katalog-ID:	DOAJ044337191

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520			\|a Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.
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allfields	10.1186/s13007-018-0352-1 doi (DE-627)DOAJ044337191 (DE-599)DOAJ25d82a33c89e4c46893194ad50a67360 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Gernot Bodner verfasserin aut Hyperspectral imaging: a novel approach for plant root phenotyping 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties. Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum Plant culture Biology (General) Alireza Nakhforoosh verfasserin aut Thomas Arnold verfasserin aut Daniel Leitner verfasserin aut In Plant Methods BMC, 2005 14(2018), 1, Seite 17 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:14 year:2018 number:1 pages:17 https://doi.org/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 kostenfrei http://link.springer.com/article/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2018 1 17
spelling	10.1186/s13007-018-0352-1 doi (DE-627)DOAJ044337191 (DE-599)DOAJ25d82a33c89e4c46893194ad50a67360 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Gernot Bodner verfasserin aut Hyperspectral imaging: a novel approach for plant root phenotyping 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties. Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum Plant culture Biology (General) Alireza Nakhforoosh verfasserin aut Thomas Arnold verfasserin aut Daniel Leitner verfasserin aut In Plant Methods BMC, 2005 14(2018), 1, Seite 17 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:14 year:2018 number:1 pages:17 https://doi.org/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 kostenfrei http://link.springer.com/article/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2018 1 17
allfields_unstemmed	10.1186/s13007-018-0352-1 doi (DE-627)DOAJ044337191 (DE-599)DOAJ25d82a33c89e4c46893194ad50a67360 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Gernot Bodner verfasserin aut Hyperspectral imaging: a novel approach for plant root phenotyping 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties. Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum Plant culture Biology (General) Alireza Nakhforoosh verfasserin aut Thomas Arnold verfasserin aut Daniel Leitner verfasserin aut In Plant Methods BMC, 2005 14(2018), 1, Seite 17 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:14 year:2018 number:1 pages:17 https://doi.org/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 kostenfrei http://link.springer.com/article/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2018 1 17
allfieldsGer	10.1186/s13007-018-0352-1 doi (DE-627)DOAJ044337191 (DE-599)DOAJ25d82a33c89e4c46893194ad50a67360 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Gernot Bodner verfasserin aut Hyperspectral imaging: a novel approach for plant root phenotyping 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties. Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum Plant culture Biology (General) Alireza Nakhforoosh verfasserin aut Thomas Arnold verfasserin aut Daniel Leitner verfasserin aut In Plant Methods BMC, 2005 14(2018), 1, Seite 17 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:14 year:2018 number:1 pages:17 https://doi.org/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 kostenfrei http://link.springer.com/article/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2018 1 17
allfieldsSound	10.1186/s13007-018-0352-1 doi (DE-627)DOAJ044337191 (DE-599)DOAJ25d82a33c89e4c46893194ad50a67360 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Gernot Bodner verfasserin aut Hyperspectral imaging: a novel approach for plant root phenotyping 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties. Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum Plant culture Biology (General) Alireza Nakhforoosh verfasserin aut Thomas Arnold verfasserin aut Daniel Leitner verfasserin aut In Plant Methods BMC, 2005 14(2018), 1, Seite 17 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:14 year:2018 number:1 pages:17 https://doi.org/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 kostenfrei http://link.springer.com/article/10.1186/s13007-018-0352-1 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2018 1 17
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authorswithroles_txt_mv	Gernot Bodner @@aut@@ Alireza Nakhforoosh @@aut@@ Thomas Arnold @@aut@@ Daniel Leitner @@aut@@
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Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. 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callnumber-first	S - Agriculture
author	Gernot Bodner
spellingShingle	Gernot Bodner misc SB1-1110 misc QH301-705.5 misc Hyperspectral imaging misc Image processing misc Phenotyping misc Root decomposition misc Triticum durum misc Plant culture misc Biology (General) Hyperspectral imaging: a novel approach for plant root phenotyping
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topic_title	SB1-1110 QH301-705.5 Hyperspectral imaging: a novel approach for plant root phenotyping Hyperspectral imaging Image processing Phenotyping Root decomposition Triticum durum
topic	misc SB1-1110 misc QH301-705.5 misc Hyperspectral imaging misc Image processing misc Phenotyping misc Root decomposition misc Triticum durum misc Plant culture misc Biology (General)
topic_unstemmed	misc SB1-1110 misc QH301-705.5 misc Hyperspectral imaging misc Image processing misc Phenotyping misc Root decomposition misc Triticum durum misc Plant culture misc Biology (General)
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title	Hyperspectral imaging: a novel approach for plant root phenotyping
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title_full	Hyperspectral imaging: a novel approach for plant root phenotyping
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author_browse	Gernot Bodner Alireza Nakhforoosh Thomas Arnold Daniel Leitner
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title_sort	hyperspectral imaging: a novel approach for plant root phenotyping
callnumber	SB1-1110
title_auth	Hyperspectral imaging: a novel approach for plant root phenotyping
abstract	Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.
abstractGer	Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.
abstract_unstemmed	Abstract Background Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. Results Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000–1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.
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title_short	Hyperspectral imaging: a novel approach for plant root phenotyping
url	https://doi.org/10.1186/s13007-018-0352-1 https://doaj.org/article/25d82a33c89e4c46893194ad50a67360 http://link.springer.com/article/10.1186/s13007-018-0352-1 https://doaj.org/toc/1746-4811
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author2	Alireza Nakhforoosh Thomas Arnold Daniel Leitner
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doi_str	10.1186/s13007-018-0352-1
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up_date	2024-07-03T22:30:02.471Z
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Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. Conclusions Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Phenotyping</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Root decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Triticum durum</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Plant culture</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biology 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Hyperspectral imaging: a novel approach for plant root phenotyping

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