Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods

Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Juanjuan Zhang [verfasserIn] Tao Cheng [verfasserIn] Wei Guo [verfasserIn] Xin Xu [verfasserIn] Hongbo Qiao [verfasserIn] Yimin Xie [verfasserIn] Xinming Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning

Übergeordnetes Werk:	In: Plant Methods - BMC, 2005, 17(2021), 1, Seite 14
Übergeordnetes Werk:	volume:17 ; year:2021 ; number:1 ; pages:14

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13007-021-00750-5

Katalog-ID:	DOAJ020088833

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520			\|a Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
650		4	\|a Winter wheat
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allfields	10.1186/s13007-021-00750-5 doi (DE-627)DOAJ020088833 (DE-599)DOAJ18b50677c7254b7ab57853fb88e98324 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Juanjuan Zhang verfasserin aut Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General) Tao Cheng verfasserin aut Wei Guo verfasserin aut Xin Xu verfasserin aut Hongbo Qiao verfasserin aut Yimin Xie verfasserin aut Xinming Ma verfasserin aut In Plant Methods BMC, 2005 17(2021), 1, Seite 14 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:17 year:2021 number:1 pages:14 https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 kostenfrei https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 17 2021 1 14
spelling	10.1186/s13007-021-00750-5 doi (DE-627)DOAJ020088833 (DE-599)DOAJ18b50677c7254b7ab57853fb88e98324 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Juanjuan Zhang verfasserin aut Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General) Tao Cheng verfasserin aut Wei Guo verfasserin aut Xin Xu verfasserin aut Hongbo Qiao verfasserin aut Yimin Xie verfasserin aut Xinming Ma verfasserin aut In Plant Methods BMC, 2005 17(2021), 1, Seite 14 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:17 year:2021 number:1 pages:14 https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 kostenfrei https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 17 2021 1 14
allfields_unstemmed	10.1186/s13007-021-00750-5 doi (DE-627)DOAJ020088833 (DE-599)DOAJ18b50677c7254b7ab57853fb88e98324 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Juanjuan Zhang verfasserin aut Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General) Tao Cheng verfasserin aut Wei Guo verfasserin aut Xin Xu verfasserin aut Hongbo Qiao verfasserin aut Yimin Xie verfasserin aut Xinming Ma verfasserin aut In Plant Methods BMC, 2005 17(2021), 1, Seite 14 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:17 year:2021 number:1 pages:14 https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 kostenfrei https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 17 2021 1 14
allfieldsGer	10.1186/s13007-021-00750-5 doi (DE-627)DOAJ020088833 (DE-599)DOAJ18b50677c7254b7ab57853fb88e98324 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Juanjuan Zhang verfasserin aut Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General) Tao Cheng verfasserin aut Wei Guo verfasserin aut Xin Xu verfasserin aut Hongbo Qiao verfasserin aut Yimin Xie verfasserin aut Xinming Ma verfasserin aut In Plant Methods BMC, 2005 17(2021), 1, Seite 14 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:17 year:2021 number:1 pages:14 https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 kostenfrei https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 17 2021 1 14
allfieldsSound	10.1186/s13007-021-00750-5 doi (DE-627)DOAJ020088833 (DE-599)DOAJ18b50677c7254b7ab57853fb88e98324 DE-627 ger DE-627 rakwb eng SB1-1110 QH301-705.5 Juanjuan Zhang verfasserin aut Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV. Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General) Tao Cheng verfasserin aut Wei Guo verfasserin aut Xin Xu verfasserin aut Hongbo Qiao verfasserin aut Yimin Xie verfasserin aut Xinming Ma verfasserin aut In Plant Methods BMC, 2005 17(2021), 1, Seite 14 (DE-627)500321191 (DE-600)2203723-8 17464811 nnns volume:17 year:2021 number:1 pages:14 https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 kostenfrei https://doi.org/10.1186/s13007-021-00750-5 kostenfrei https://doaj.org/toc/1746-4811 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 17 2021 1 14
language	English
source	In Plant Methods 17(2021), 1, Seite 14 volume:17 year:2021 number:1 pages:14
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning Plant culture Biology (General)
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container_title	Plant Methods
authorswithroles_txt_mv	Juanjuan Zhang @@aut@@ Tao Cheng @@aut@@ Wei Guo @@aut@@ Xin Xu @@aut@@ Hongbo Qiao @@aut@@ Yimin Xie @@aut@@ Xinming Ma @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
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Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. 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author	Juanjuan Zhang
spellingShingle	Juanjuan Zhang misc SB1-1110 misc QH301-705.5 misc Winter wheat misc Leaf area index misc Unmanned aerial vehicle misc Hyperspectral imaging data misc Characteristic bands misc Machine learning misc Plant culture misc Biology (General) Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
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topic_title	SB1-1110 QH301-705.5 Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods Winter wheat Leaf area index Unmanned aerial vehicle Hyperspectral imaging data Characteristic bands Machine learning
topic	misc SB1-1110 misc QH301-705.5 misc Winter wheat misc Leaf area index misc Unmanned aerial vehicle misc Hyperspectral imaging data misc Characteristic bands misc Machine learning misc Plant culture misc Biology (General)
topic_unstemmed	misc SB1-1110 misc QH301-705.5 misc Winter wheat misc Leaf area index misc Unmanned aerial vehicle misc Hyperspectral imaging data misc Characteristic bands misc Machine learning misc Plant culture misc Biology (General)
topic_browse	misc SB1-1110 misc QH301-705.5 misc Winter wheat misc Leaf area index misc Unmanned aerial vehicle misc Hyperspectral imaging data misc Characteristic bands misc Machine learning misc Plant culture misc Biology (General)
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title	Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
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title_full	Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
author_sort	Juanjuan Zhang
journal	Plant Methods
journalStr	Plant Methods
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author_browse	Juanjuan Zhang Tao Cheng Wei Guo Xin Xu Hongbo Qiao Yimin Xie Xinming Ma
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author-letter	Juanjuan Zhang
doi_str_mv	10.1186/s13007-021-00750-5
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title_sort	leaf area index estimation model for uav image hyperspectral data based on wavelength variable selection and machine learning methods
callnumber	SB1-1110
title_auth	Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
abstract	Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
abstractGer	Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
abstract_unstemmed	Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
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title_short	Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
url	https://doi.org/10.1186/s13007-021-00750-5 https://doaj.org/article/18b50677c7254b7ab57853fb88e98324 https://doaj.org/toc/1746-4811
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author2	Tao Cheng Wei Guo Xin Xu Hongbo Qiao Yimin Xie Xinming Ma
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