# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
###### Abstract  
This study was conducted to evaluate the potential of low-cost hyperspectral sensors for the early detection of Flavescence doree (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 Gargangea. 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. precision farming; vineyard; hyperspectral remote sensing; low-cost spectrometer; machine learning algorithms; feature selection; genetic algorithms (GA) +
Footnote †: journal: Medical Imaging  
symptons caused by other non-quarantine pathogens, namely the Bois Noir phytoplasma (BNp), may be misleading; the timing of symptoms' appearance may vary among seasons and varieties [7]. As a consequence, infected plants may escape early removal and become inoculum sources for neighboring plants, while non-infected plants with similar symptoms may be unnecessarily removed. A reliable diagnosis can only be achieved following molecular detection of pathogen DNA, by polymerase chain reaction (nested PCR) or quantitative real-time PCR [1]. These lab-based methods involve destructive sampling, are time-consuming and expensive, and require skilled personnel to be carried out in a reliable, sensitive, and versatile manner. The recent advancements in sensor technologies including hyperspectral spectrometers together with machine learning (ML) methods boost their applications in precision agriculture [8]. ML methods are based on computational algorithms that can learn from the input data and perform classification or clustering tasks, which are suitable to identify the patterns and trends of large amounts of data such as hyperspectral data. In this regard, optical sensing technology has proved to be a potentially very useful tool that used in precision agriculture especially for the diagnosis of plant diseases before they become visible to the human eye [9; 10; 11; 12]. This optical technology and novel sensors measure the reflectance characteristics of plants in different regions of the electromagnetic spectrum by evaluating the interaction between plant leaves and incoming solar radiation. The reflectance (the ratio between the intensities of incoming and reflected light) provides information about the leaf health status and allows to detect the first changes in the physiology of plants caused by biotic stresses.