Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782268957615456256 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3649375 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sankaransindhuja huanglongbingcitrusgreeningdetectionusingvisiblenearinfraredandthermalimagingtechniques AT majajoemari huanglongbingcitrusgreeningdetectionusingvisiblenearinfraredandthermalimagingtechniques AT buchanonsherrie huanglongbingcitrusgreeningdetectionusingvisiblenearinfraredandthermalimagingtechniques AT ehsanireza huanglongbingcitrusgreeningdetectionusingvisiblenearinfraredandthermalimagingtechniques |