Low-Cost Handheld Spectrometry for Detecting Flavescence Dorée in Vineyards

Abstract

This study was conducted to evaluate the potential of low-cost hyperspectral sensors for the early detection of Flavescence dorée (FD) from asymptomatic samples prior to symptom development. In total, 180 leaf spectra from 60 randomly selected plants (three leaves per plant) were collected by using two portable mini-spectrometers (Hamamatsu: 340–850 nm and NIRScan: 900–1700 nm) at five vegetative growth stages in a vineyard with grape variety Garganega. High differences in the Hamamatsu spectra of the two groups were found in the VIS-NIR (visible–near infrared) spectral region while very small differences were observed in the NIRScan spectra. We analyzed the spectral data of two sensors by using all bands, features reduced by an ensemble method, and by genetic algorithms (GA) to discriminate the asymptomatic healthy (FD negative) and diseased (FD positive) leaves using five different classifiers. Overall, high classification accuracies were found in case of the Hamamatsu sensor compared to the NIRScan sensor. The feature selection techniques performed better compared to all bands, and the highest classification accuracy of 96% was achieved when GA features of the Hamamatsu sensor were used with the logistic regression (LR) classifier on test samples. A slightly low accuracy of 85% was achieved when the features (selected by the ensemble method) of the Hamamatsu sensor were used with the support vector machine (SVM) classifier by using leave-one-out (LOO) cross-validation on the whole dataset. Results demonstrated that employing a feature selection technique can provide a valid tool for determining the optimal bands that can be used to identify FD disease in the vineyard. However, further validation studies are required, as this study was conducted using a small dataset and from the single grapevine variety.

Keywords

• precision farming
• vineyard
• hyperspectral remote sensing
• low-cost spectrometer
• machine learning algorithms
• feature selection