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Journal of Spectroscopy
Volume 2013, Article ID 841738, 6 pages
http://dx.doi.org/10.1155/2013/841738
Research Article

A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil

1Instituto de Física de São Carlos, Universidade de São Paulo, Cx. Postal 369, 13560-970 São Carlos, SP, Brazil
2Citrus Research and Education Center, IFAS, University of Florida, 700 Experiment Station Road, Lake Alfred, FL 33850, USA
3Departamento Científico, Fundecitrus, Avenida Dr. Adhemar P. de Barros, 20114 807-040 Araraquara, SP, Brazil

Received 5 June 2012; Accepted 29 October 2012

Academic Editor: Luciano Bachmann

Copyright © 2013 Caio B. Wetterich et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.