A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil
The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2013/841738 |
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| author | Caio B. Wetterich Ratnesh Kumar Sindhuja Sankaran José Belasque Junior Reza Ehsani Luis G. Marcassa |
| author_facet | Caio B. Wetterich Ratnesh Kumar Sindhuja Sankaran José Belasque Junior Reza Ehsani Luis G. Marcassa |
| author_sort | Caio B. Wetterich |
| collection | DOAJ |
| description | The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture features were extracted from the segmented images using cooccurrence matrix. The extracted features were used as an input into the classifier, support vector machine (SVM). The classification results were evaluated based on classification accuracies and number of false positives and false negatives. The results indicated that the SVM could classify HLB-infected leaf fluorescence intensities with up to 90% classification accuracy. Though the fluorescence intensities from leaves collected in Brazil and the USA were different, the method shows potential for detecting HLB. |
| format | Article |
| id | doaj-art-fd074aea07224f228bbb3bb17e796b2f |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-fd074aea07224f228bbb3bb17e796b2f2025-02-03T00:59:44ZengWileyJournal of Spectroscopy2314-49202314-49392013-01-01201310.1155/2013/841738841738A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and BrazilCaio B. Wetterich0Ratnesh Kumar1Sindhuja Sankaran2José Belasque Junior3Reza Ehsani4Luis G. Marcassa5Instituto de Física de São Carlos, Universidade de São Paulo, Cx. Postal 369, 13560-970 São Carlos, SP, BrazilCitrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USACitrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USADepartamento Científico, Fundecitrus, Avenida Dr. Adhemar P. de Barros, 20114 807-040 Araraquara, SP, BrazilCitrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USAInstituto de Física de São Carlos, Universidade de São Paulo, Cx. Postal 369, 13560-970 São Carlos, SP, BrazilThe overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture features were extracted from the segmented images using cooccurrence matrix. The extracted features were used as an input into the classifier, support vector machine (SVM). The classification results were evaluated based on classification accuracies and number of false positives and false negatives. The results indicated that the SVM could classify HLB-infected leaf fluorescence intensities with up to 90% classification accuracy. Though the fluorescence intensities from leaves collected in Brazil and the USA were different, the method shows potential for detecting HLB.http://dx.doi.org/10.1155/2013/841738 |
| spellingShingle | Caio B. Wetterich Ratnesh Kumar Sindhuja Sankaran José Belasque Junior Reza Ehsani Luis G. Marcassa A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil Journal of Spectroscopy |
| title | A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil |
| title_full | A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil |
| title_fullStr | A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil |
| title_full_unstemmed | A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil |
| title_short | A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil |
| title_sort | comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of huanglongbing citrus disease in the usa and brazil |
| url | http://dx.doi.org/10.1155/2013/841738 |
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