Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images

Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies sap...
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Autor*in:	Sell, Marili [verfasserIn] Smith, Abraham George Burdun, Iuliia Rohula-Okunev, Gristin Kupper, Priit Ostonen, Ivika

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Deep learning AI RootPainter Fine roots Climate change

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022

Übergeordnetes Werk:	Enthalten in: Plant and soil - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948, 480(2022), 1-2 vom: 25. Juni, Seite 135-150
Übergeordnetes Werk:	volume:480 ; year:2022 ; number:1-2 ; day:25 ; month:06 ; pages:135-150

Links:	Volltext

DOI / URN:	10.1007/s11104-022-05565-4

Katalog-ID:	SPR048744824

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520			\|a Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments.
650		4	\|a Deep learning \|7 (dpeaa)DE-He213
650		4	\|a AI \|7 (dpeaa)DE-He213
650		4	\|a RootPainter \|7 (dpeaa)DE-He213
650		4	\|a Fine roots \|7 (dpeaa)DE-He213
650		4	\|a Climate change \|7 (dpeaa)DE-He213
700	1		\|a Smith, Abraham George \|4 aut
700	1		\|a Burdun, Iuliia \|4 aut
700	1		\|a Rohula-Okunev, Gristin \|4 aut
700	1		\|a Kupper, Priit \|4 aut
700	1		\|a Ostonen, Ivika \|4 aut
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author_variant	m s ms a g s ag ags i b ib g r o gro p k pk i o io
matchkey_str	article:15735036:2022----::sesnteieotrwhyaisfowypueaiuaebaruiiynsintoewtdela
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publishDate	2022
allfields	10.1007/s11104-022-05565-4 doi (DE-627)SPR048744824 (SPR)s11104-022-05565-4-e DE-627 ger DE-627 rakwb eng Sell, Marili verfasserin (orcid)0000-0002-3243-0107 aut Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Smith, Abraham George aut Burdun, Iuliia aut Rohula-Okunev, Gristin aut Kupper, Priit aut Ostonen, Ivika aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 480(2022), 1-2 vom: 25. Juni, Seite 135-150 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150 https://dx.doi.org/10.1007/s11104-022-05565-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 480 2022 1-2 25 06 135-150
spelling	10.1007/s11104-022-05565-4 doi (DE-627)SPR048744824 (SPR)s11104-022-05565-4-e DE-627 ger DE-627 rakwb eng Sell, Marili verfasserin (orcid)0000-0002-3243-0107 aut Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Smith, Abraham George aut Burdun, Iuliia aut Rohula-Okunev, Gristin aut Kupper, Priit aut Ostonen, Ivika aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 480(2022), 1-2 vom: 25. Juni, Seite 135-150 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150 https://dx.doi.org/10.1007/s11104-022-05565-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 480 2022 1-2 25 06 135-150
allfields_unstemmed	10.1007/s11104-022-05565-4 doi (DE-627)SPR048744824 (SPR)s11104-022-05565-4-e DE-627 ger DE-627 rakwb eng Sell, Marili verfasserin (orcid)0000-0002-3243-0107 aut Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Smith, Abraham George aut Burdun, Iuliia aut Rohula-Okunev, Gristin aut Kupper, Priit aut Ostonen, Ivika aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 480(2022), 1-2 vom: 25. Juni, Seite 135-150 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150 https://dx.doi.org/10.1007/s11104-022-05565-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 480 2022 1-2 25 06 135-150
allfieldsGer	10.1007/s11104-022-05565-4 doi (DE-627)SPR048744824 (SPR)s11104-022-05565-4-e DE-627 ger DE-627 rakwb eng Sell, Marili verfasserin (orcid)0000-0002-3243-0107 aut Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Smith, Abraham George aut Burdun, Iuliia aut Rohula-Okunev, Gristin aut Kupper, Priit aut Ostonen, Ivika aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 480(2022), 1-2 vom: 25. Juni, Seite 135-150 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150 https://dx.doi.org/10.1007/s11104-022-05565-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 480 2022 1-2 25 06 135-150
allfieldsSound	10.1007/s11104-022-05565-4 doi (DE-627)SPR048744824 (SPR)s11104-022-05565-4-e DE-627 ger DE-627 rakwb eng Sell, Marili verfasserin (orcid)0000-0002-3243-0107 aut Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Smith, Abraham George aut Burdun, Iuliia aut Rohula-Okunev, Gristin aut Kupper, Priit aut Ostonen, Ivika aut Enthalten in Plant and soil Dordrecht [u.a.] : Springer Science + Business Media B.V, 1948 480(2022), 1-2 vom: 25. Juni, Seite 135-150 (DE-627)270934979 (DE-600)1478535-3 1573-5036 nnns volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150 https://dx.doi.org/10.1007/s11104-022-05565-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 480 2022 1-2 25 06 135-150
language	English
source	Enthalten in Plant and soil 480(2022), 1-2 vom: 25. Juni, Seite 135-150 volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150
sourceStr	Enthalten in Plant and soil 480(2022), 1-2 vom: 25. Juni, Seite 135-150 volume:480 year:2022 number:1-2 day:25 month:06 pages:135-150
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Deep learning AI RootPainter Fine roots Climate change
isfreeaccess_bool	false
container_title	Plant and soil
authorswithroles_txt_mv	Sell, Marili @@aut@@ Smith, Abraham George @@aut@@ Burdun, Iuliia @@aut@@ Rohula-Okunev, Gristin @@aut@@ Kupper, Priit @@aut@@ Ostonen, Ivika @@aut@@
publishDateDaySort_date	2022-06-25T00:00:00Z
hierarchy_top_id	270934979
id	SPR048744824
language_de	englisch
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author	Sell, Marili
spellingShingle	Sell, Marili misc Deep learning misc AI misc RootPainter misc Fine roots misc Climate change Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
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topic_title	Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images Deep learning (dpeaa)DE-He213 AI (dpeaa)DE-He213 RootPainter (dpeaa)DE-He213 Fine roots (dpeaa)DE-He213 Climate change (dpeaa)DE-He213
topic	misc Deep learning misc AI misc RootPainter misc Fine roots misc Climate change
topic_unstemmed	misc Deep learning misc AI misc RootPainter misc Fine roots misc Climate change
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title	Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
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title_full	Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
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author_browse	Sell, Marili Smith, Abraham George Burdun, Iuliia Rohula-Okunev, Gristin Kupper, Priit Ostonen, Ivika
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title_sort	assessing the fine root growth dynamics of norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
title_auth	Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
abstract	Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
abstractGer	Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
abstract_unstemmed	Purpose Measuring continuous root growth with less time- and resource-intensive methods is challenging for plant ecologists. We propose an approach where photos of fine root growth were taken with smartphones and analyzed by the deep learning method-based program RootPainter. Methods Picea abies saplings were grown in transparent boxes in growth chambers at either moderate (mRH) or elevated air humidity (eRH) and in nitrate ($ NO_{3} $−) or ammonium ($ NH_{4} $+) dominated soil. To evaluate the partitioning of roots, we analyzed both fine root projection area (PA) of total and young white roots. We aimed to measure the ability of RootPainter to pick up on variation in fine root growth caused by changes in air humidity and soil nitrogen source. Results Trees growing at mRH-$ NH_{4} $+ had the highest total PA, 9.4 ± 1.9 $ cm^{2} $, while the lowest was in trees growing at eRH-$ NO_{3} $−, 3.9 ± 0.6 $ cm^{2} $. The young root PA was highest at the beginning of the experiment and was affected by the initial soil nitrogen source. The older (brown) root PA increased over time. At mRH, the relative growth peak of root browning followed the peak of white roots, while at eRH, the peaks coincided. We found strong agreement between the automatic segmentation and manual annotation (F-measure = 0.88); the error measures showed no treatment-specific bias. Conclusions We conclude that the increased air humidity reduced the fine root growth and diminished sequential developmental peaks. The combination of smartphone images and RootPainter gives reliable results and is easy to use in future plant growth manipulation experiments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
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container_issue	1-2
title_short	Assessing the fine root growth dynamics of Norway spruce manipulated by air humidity and soil nitrogen with deep learning segmentation of smartphone images
url	https://dx.doi.org/10.1007/s11104-022-05565-4
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author2	Smith, Abraham George Burdun, Iuliia Rohula-Okunev, Gristin Kupper, Priit Ostonen, Ivika
author2Str	Smith, Abraham George Burdun, Iuliia Rohula-Okunev, Gristin Kupper, Priit Ostonen, Ivika
ppnlink	270934979
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doi_str	10.1007/s11104-022-05565-4
up_date	2024-07-03T21:12:50.733Z
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