# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
###### Abstract
The reflectance of the leaves in the visible spectral regions (VIS) is related to leaf pigment contents and the reflectance in near infrared (NIR) is typically related to leaf cell structure and water contents [13; 14; 15]. FD infection is known to cause metabolic alterations in grapevine leaves [16] which in turn can alter the foliar optical properties several days before the appearance of the first visible symptoms on the leaves. The high dimensionality of the hyperspectral data is a critical factor that needs to be carefully considered when analyzing the hyperspectral data [17]. High dimensionality of the datasets can be effectively reduced (also called feature selection) by searching for a subset of wavebands containing salient spectral information and by removing non-pertinent, noisy, and redundant features [18]. [PERSON] et al. [19] exploited GA to investigate the optimal spectral bands to be selected for FD. In another study, [PERSON] et al. [20] presented in-house developed multispectral camera and highlighted the importance of feature selection approach to select most the optimal spectral bands to detect FD in vineyards. Several studies have demonstrated the potential of the optical sensor to detect disease FD in grapevines [4; 6; 21; 22]. [PERSON] et al. [4] investigated the optimal spectral bands for designing a FD-specific spectral sensor to detect the FD in the grapevine field. In this study, authors used feature selection techniques based on the successive projection algorithm (SPA) and the traditional vegetation indices (SVIs) for determining the optimal bands that are sensitive to FD symptomatic plants. [PERSON] et al. [21] tested the potential of SVIs calculated from a UAV multispectral sensor (MicaSense RedEdge-MXM\({}^{\text{TM}}\), MicaSense, Inc., Northlake Way, Seattle, WA, USA) and biophysical parameters (pigment content) to detect abnormal vegetation behaviour in relation to FD and grapevine trunk diseases at various levels of infections. The authors demonstrated that SVIs based on red and green spectral bands effectively map the infected plants when their level of infection is greater than 50%. Likewise in a recent study, [PERSON] et al. [22] highlighted the potential of using a ground-based multispectral sensor (OptRx\({}^{\text{\textregistered}}\)--Ag Leader, South Riverside Drive Ames, IA, USA) to detect the FD and proposed SVIs which are sensitive to detect the FD. The authors of the study used two SVIs (normalized difference vegetation index: NDVI and normalized difference red-edge index: NDE) and reported that the infected plants showed lower values compared to healthy plants. However, these studies were conducted either at a single point during the symptomatic phase of the disease or used multispectral sensors with few bands. The ground-based optical spectroscopy together with ML methods is an interesting approach for rapid monitoring of crop operations and especially plant disease detection [8]but at the same time, there are some constraints to using hyperspectral sensors including portability and their greater equipment cost [23]. Recent advances in field-based spectroscopy have emerged to develop low-cost portable hyperspectral spectrometer devices that provide spectral data for rapid field-based assessments of plant health status. In this regard, many authors presented studies in which they highlighted the necessity to develop such low-cost spectrometer devices which can be used as an alternative to an expensive and time-consuming wet-chemistry analysis. The aim of this study is to ascertain whether the spectral features extracted from asymptomatic samples could be used to differentiate healthy vines from diseased vines. In the present study, we demonstrate the ability of low-cost hyperspectral sensors combined with ML algorithms for rapid, and cost-effective in-field automated detection of the FD disease. The specific objectives of the study are:  
* To compare the performances of the two portable mini spectrometers to detect the FD in vineyards.