Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging

Abstract

This research was conducted to examine the potential use of hyperspectral imaging for non-destructive detection of Grapevine leafroll-associated virus 3 (GLRaV-3) during asymptomatic and symptomatic stages of grapevine leafroll disease (GLD) in a red-berried wine grape (Vitis vinifera) cultivar. Cabernet Sauvignon vines tested positive and negative for GLRaV-3 were used in this study. Leaves from infected and non-infected vines were detached at five phenological stages and individual leaf images were acquired by a hyperspectral imager in 2017, 2018, and 2019 seasons. Those images were then preprocessed using spectra normalization and Monte-Carlo method for eliminating spectral sample outliers. Least absolute shrinkage and selection operator was used to select feature wavelengths for each phenological stage within three-season datasets. The sensitivity of selected feature wavelengths was evaluated based on analysis of variance and linear regression. Six salient wavelengths (690, 715, 731, 1409, 1425 and 1582 nm) were determined as sensitive wavebands for detecting virus symptoms in leaf samples. The detectability of GLD using those six salient wavelengths was evaluated using least squares-support vector machine. The classification accuracy was found between 66.67 and 89.93% for test datasets collected at first asymptomatic stage over three seasons. These results indicated that the hyperspectral imaging technique has the potential for nondestructively detecting virus-infected grapevines during asymptomatic stages.