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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/2/2117 |
| _version_ | 1798040107928780800 |
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| author | Reza Ehsani Sherrie Buchanon Joe Mari Maja Sindhuja Sankaran |
| author_facet | Reza Ehsani Sherrie Buchanon Joe Mari Maja Sindhuja Sankaran |
| author_sort | Reza Ehsani |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T22:02:57Z |
| format | Article |
| id | doaj.art-2617eb72fb2048e4971d5395afbcce0b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:02:57Z |
| publishDate | 2013-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2617eb72fb2048e4971d5395afbcce0b2022-12-22T04:00:49ZengMDPI AGSensors1424-82202013-02-011322117213010.3390/s130202117Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging TechniquesReza EhsaniSherrie BuchanonJoe Mari MajaSindhuja SankaranThis 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.http://www.mdpi.com/1424-8220/13/2/2117citrus diseasevisible-near infrared imagingthermal imagingsupport vector machine |
| spellingShingle | Reza Ehsani Sherrie Buchanon Joe Mari Maja Sindhuja Sankaran Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques Sensors citrus disease visible-near infrared imaging thermal imaging support vector machine |
| 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 | citrus disease visible-near infrared imaging thermal imaging support vector machine |
| url | http://www.mdpi.com/1424-8220/13/2/2117 |
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