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Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques

This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data a...

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Autores principales: Sankaran, Sindhuja, Maja, Joe Mari, Buchanon, Sherrie, Ehsani, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649375/
https://www.ncbi.nlm.nih.gov/pubmed/23389343
http://dx.doi.org/10.3390/s130202117
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author Sankaran, Sindhuja
Maja, Joe Mari
Buchanon, Sherrie
Ehsani, Reza
author_facet Sankaran, Sindhuja
Maja, Joe Mari
Buchanon, Sherrie
Ehsani, Reza
author_sort Sankaran, Sindhuja
collection PubMed
description This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves.
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spelling pubmed-36493752013-06-04 Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques Sankaran, Sindhuja Maja, Joe Mari Buchanon, Sherrie Ehsani, Reza Sensors (Basel) Article This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves. Molecular Diversity Preservation International (MDPI) 2013-02-06 /pmc/articles/PMC3649375/ /pubmed/23389343 http://dx.doi.org/10.3390/s130202117 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Sankaran, Sindhuja
Maja, Joe Mari
Buchanon, Sherrie
Ehsani, Reza
Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title_full Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title_fullStr Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title_full_unstemmed Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title_short Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
title_sort huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649375/
https://www.ncbi.nlm.nih.gov/pubmed/23389343
http://dx.doi.org/10.3390/s130202117
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