Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters

Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The ro...
Ausführliche Beschreibung

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Autor*in:	Jiangsan Zhao [verfasserIn] Dmitry Kechasov [verfasserIn] Boris Rewald [verfasserIn] Gernot Bodner [verfasserIn] Michel Verheul [verfasserIn] Nicholas Clarke [verfasserIn] Jihong Liu Clarke [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	hyperspectral image reconstruction RGB image deep learning HSCNN-R SSC TTA

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 19, p 3258
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:19, p 3258

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12193258

Katalog-ID:	DOAJ034630848

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allfields	10.3390/rs12193258 doi (DE-627)DOAJ034630848 (DE-599)DOAJ2cc990035a8a4eaca5f7925cf8835b8d DE-627 ger DE-627 rakwb eng Jiangsan Zhao verfasserin aut Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond. hyperspectral image reconstruction RGB image deep learning HSCNN-R SSC TTA Science Q Dmitry Kechasov verfasserin aut Boris Rewald verfasserin aut Gernot Bodner verfasserin aut Michel Verheul verfasserin aut Nicholas Clarke verfasserin aut Jihong Liu Clarke verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 19, p 3258 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:19, p 3258 https://doi.org/10.3390/rs12193258 kostenfrei https://doaj.org/article/2cc990035a8a4eaca5f7925cf8835b8d kostenfrei https://www.mdpi.com/2072-4292/12/19/3258 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 12 2020 19, p 3258
spelling	10.3390/rs12193258 doi (DE-627)DOAJ034630848 (DE-599)DOAJ2cc990035a8a4eaca5f7925cf8835b8d DE-627 ger DE-627 rakwb eng Jiangsan Zhao verfasserin aut Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond. hyperspectral image reconstruction RGB image deep learning HSCNN-R SSC TTA Science Q Dmitry Kechasov verfasserin aut Boris Rewald verfasserin aut Gernot Bodner verfasserin aut Michel Verheul verfasserin aut Nicholas Clarke verfasserin aut Jihong Liu Clarke verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 19, p 3258 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:19, p 3258 https://doi.org/10.3390/rs12193258 kostenfrei https://doaj.org/article/2cc990035a8a4eaca5f7925cf8835b8d kostenfrei https://www.mdpi.com/2072-4292/12/19/3258 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 12 2020 19, p 3258
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allfieldsSound	10.3390/rs12193258 doi (DE-627)DOAJ034630848 (DE-599)DOAJ2cc990035a8a4eaca5f7925cf8835b8d DE-627 ger DE-627 rakwb eng Jiangsan Zhao verfasserin aut Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond. hyperspectral image reconstruction RGB image deep learning HSCNN-R SSC TTA Science Q Dmitry Kechasov verfasserin aut Boris Rewald verfasserin aut Gernot Bodner verfasserin aut Michel Verheul verfasserin aut Nicholas Clarke verfasserin aut Jihong Liu Clarke verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 19, p 3258 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:19, p 3258 https://doi.org/10.3390/rs12193258 kostenfrei https://doaj.org/article/2cc990035a8a4eaca5f7925cf8835b8d kostenfrei https://www.mdpi.com/2072-4292/12/19/3258 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 12 2020 19, p 3258
language	English
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authorswithroles_txt_mv	Jiangsan Zhao @@aut@@ Dmitry Kechasov @@aut@@ Boris Rewald @@aut@@ Gernot Bodner @@aut@@ Michel Verheul @@aut@@ Nicholas Clarke @@aut@@ Jihong Liu Clarke @@aut@@
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author	Jiangsan Zhao
spellingShingle	Jiangsan Zhao misc hyperspectral image reconstruction misc RGB image misc deep learning misc HSCNN-R misc SSC misc TTA misc Science misc Q Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
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topic_title	Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters hyperspectral image reconstruction RGB image deep learning HSCNN-R SSC TTA
topic	misc hyperspectral image reconstruction misc RGB image misc deep learning misc HSCNN-R misc SSC misc TTA misc Science misc Q
topic_unstemmed	misc hyperspectral image reconstruction misc RGB image misc deep learning misc HSCNN-R misc SSC misc TTA misc Science misc Q
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title	Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
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title_full	Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
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title_sort	deep learning in hyperspectral image reconstruction from single rgb images—a case study on tomato quality parameters
title_auth	Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
abstract	Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
abstractGer	Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
abstract_unstemmed	Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.
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title_short	Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters
url	https://doi.org/10.3390/rs12193258 https://doaj.org/article/2cc990035a8a4eaca5f7925cf8835b8d https://www.mdpi.com/2072-4292/12/19/3258 https://doaj.org/toc/2072-4292
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author2	Dmitry Kechasov Boris Rewald Gernot Bodner Michel Verheul Nicholas Clarke Jihong Liu Clarke
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