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Diagnosis of CTV-Infected Leaves Using Hyperspectral Imaging



Hyperspectral reflectance images of healthy and diseased leaves infected with different isolates of Citrus tristeza virus (CTV) including TRL514, CT30, CT32 and CT11A were collected in the visible and near-infrared region of 4002013100000A0nm. Average reflectance spectrum was generated from each hyperspectral image individually obtained from 60 healthy and 240 CTV-infected leaves. The spectra were transformed with 15-point Savitzky Golay second derivative. Then principal component analysis was performed on the transformed data in order to reduce the dimension of data. Comparative analysis was performed among supervised classification models, including back-propagation neural network (BPNN), linear discriminant analysis (LDA) and Mahalanobis distance (MD). When the second derivative spectra were analyzed, classifier models including BPNN, LDA and MD can discriminate the healthy and CTV-infected leaves with the highest classification accuracies of 100% in the spectral range of 4002013100000A0nm and 7602013100000A0nm. Nine optimal wavelengths (405, 424, 920, 947, 957, 972, 978, 980, and 99800A0nm) selected by stepwise regression resulted in 97.33% total classification accuracy for differentiation of healthy and CTV-infected leaves and showed great potential in CTV diagnosis. However, the overall classification accuracy of different CTV isolates infected leaves resulted in 70% based on the MD model using the selected optimal wavelengths. Further study is required to find out whether the method is suitable for CTV detection under field conditions.



Total Pages: 15
Pages: 269-283


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Volume: 21
Issue: 3
Year: 2015

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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