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Visible−near infrared spectroscopy−based detection of Grapevine leafroll−associated virus 3 in a red−fruited wine grape cultivar

Sinha, Rajeev, Khot, Lav R., Rathnayake, Anura P., Gao, Zongmei, Naidu, Rayapati A.
Computers and electronics in agriculture 2019
Grapevine leafroll-associated virus 3, Vitis vinifera, cultivars, discriminant analysis, fiber optics, financial economics, growers, least squares, leaves, phenology, plant diseases and disorders, plant viruses, spectroradiometers, spectroscopy, viruses, wavelengths, wine grapes
Grapevine leafroll disease (GLD) is one of the major threats to wine grapes (Vitis vinifera) causing substantial economic losses to growers. This study was undertaken to evaluate the applicability of visible and near infrared (VIS−NIR) spectrometry as a rapid, robust and non–destructive optical sensing method for the detection of Grapevine leafroll−associated virus 3 (GLRaV-3) at different phenological stages in a red−berried wine grape cultivar. Using VIS−NIR spectroradiometer, data was collected from healthy and GLRaV-3 −infected leaf samples from cv. Cabernet sauvignon for two seasons at specific intervals during asymptomatic and symptomatic stages of the disease. Fiber optic leaf clip was used to collect spectral responses from grapevine leaves under field conditions. Salient feature extraction using stepwise multilinear regression and partial least square regression methods showed significant differences between healthy and virus–infected leaves in the visible (351, 377, 501, 526, 626, and 676 nm) and near infrared (701, 726, 826, 901, 951, 976, 1001, 1027, 1052 and 1101 nm) regions. Spectral wavelengths from near infrared region (1001, 1027 and 1052 nm) were validated at different phenological stages spanning both asymptomatic and symptomatic stages of GLD. Selected spectral wavelengths demonstrated robustness in virus detection with overall classification accuracies in the range of 75–99% using quadratic discriminant analysis (QDA) classifier. QDA based classification accuracies for healthy, infected and overall classes were significantly higher compared to Naïve Bayes classifier. The classification accuracy for virus detection during asymptomatic stages was not significantly different from symptomatic phase, indicating reliability of the selected features for early detection of GLRaV–3–infected grapevines.