Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery

Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dong-Wook Kim [verfasserIn] Hee Sup Yun [verfasserIn] Sang-Jin Jeong [verfasserIn] Young-Seok Kwon [verfasserIn] Suk-Gu Kim [verfasserIn] Won Suk Lee [verfasserIn] Hak-Jin Kim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	unmanned aerial vehicle RGB vegetation fraction plant height structure from motion crop surface model

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 10(2018), 4, p 563
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:4, p 563

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs10040563

Katalog-ID:	DOAJ073773263

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allfieldsSound	10.3390/rs10040563 doi (DE-627)DOAJ073773263 (DE-599)DOAJb764917b1eaa476e832c7499a64c7ec8 DE-627 ger DE-627 rakwb eng Dong-Wook Kim verfasserin aut Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season. unmanned aerial vehicle RGB vegetation fraction plant height structure from motion crop surface model Science Q Hee Sup Yun verfasserin aut Sang-Jin Jeong verfasserin aut Young-Seok Kwon verfasserin aut Suk-Gu Kim verfasserin aut Won Suk Lee verfasserin aut Hak-Jin Kim verfasserin aut In Remote Sensing MDPI AG, 2009 10(2018), 4, p 563 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:10 year:2018 number:4, p 563 https://doi.org/10.3390/rs10040563 kostenfrei https://doaj.org/article/b764917b1eaa476e832c7499a64c7ec8 kostenfrei http://www.mdpi.com/2072-4292/10/4/563 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 10 2018 4, p 563
language	English
source	In Remote Sensing 10(2018), 4, p 563 volume:10 year:2018 number:4, p 563
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institution	findex.gbv.de
topic_facet	unmanned aerial vehicle RGB vegetation fraction plant height structure from motion crop surface model Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Dong-Wook Kim @@aut@@ Hee Sup Yun @@aut@@ Sang-Jin Jeong @@aut@@ Young-Seok Kwon @@aut@@ Suk-Gu Kim @@aut@@ Won Suk Lee @@aut@@ Hak-Jin Kim @@aut@@
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author	Dong-Wook Kim
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issn	20724292
topic_title	Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery unmanned aerial vehicle RGB vegetation fraction plant height structure from motion crop surface model
topic	misc unmanned aerial vehicle misc RGB misc vegetation fraction misc plant height misc structure from motion misc crop surface model misc Science misc Q
topic_unstemmed	misc unmanned aerial vehicle misc RGB misc vegetation fraction misc plant height misc structure from motion misc crop surface model misc Science misc Q
topic_browse	misc unmanned aerial vehicle misc RGB misc vegetation fraction misc plant height misc structure from motion misc crop surface model misc Science misc Q
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title	Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery
ctrlnum	(DE-627)DOAJ073773263 (DE-599)DOAJb764917b1eaa476e832c7499a64c7ec8
title_full	Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery
author_sort	Dong-Wook Kim
journal	Remote Sensing
journalStr	Remote Sensing
lang_code	eng
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author_browse	Dong-Wook Kim Hee Sup Yun Sang-Jin Jeong Young-Seok Kwon Suk-Gu Kim Won Suk Lee Hak-Jin Kim
container_volume	10
format_se	Elektronische Aufsätze
author-letter	Dong-Wook Kim
doi_str_mv	10.3390/rs10040563
author2-role	verfasserin
title_sort	modeling and testing of growth status for chinese cabbage and white radish with uav-based rgb imagery
title_auth	Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery
abstract	Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
abstractGer	Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
abstract_unstemmed	Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
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container_issue	4, p 563
title_short	Modeling and Testing of Growth Status for Chinese Cabbage and White Radish with UAV-Based RGB Imagery
url	https://doi.org/10.3390/rs10040563 https://doaj.org/article/b764917b1eaa476e832c7499a64c7ec8 http://www.mdpi.com/2072-4292/10/4/563 https://doaj.org/toc/2072-4292
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author2	Hee Sup Yun Sang-Jin Jeong Young-Seok Kwon Suk-Gu Kim Won Suk Lee Hak-Jin Kim
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up_date	2024-07-03T19:32:38.352Z
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