Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’

Disease detection and control is one of the main objectives of vineyard research in France. Manual monitoring of diseases is a time consuming operation especially when fields are large. Current research studies aim to develop automatic in-field systems to detect diseases. This study investigated the optimal spectral bands for the design of a dedicated high-resolution multispectral camera embedded on an unmanned aerial vehicle for identifying infected zones in a grapevine field. The target disease was Flavescence doree, which is infectious, incurable and can result in considerable yield loss. An in-field spectrometry study was performed on four grapevine varieties in the Provence Alpes Cote d’Azur region in France. Two spectral analysis techniques were proposed for choosing the best spectral bands capable of discriminating healthy from diseased leaves. The first novel approach is a feature selection technique based on the successive projection algorithm (SPA), some spectral pre-processing techniques were jointly investigated. The second approach examines a set of traditional vegetation indices (VI). Support vector machine (SVM) and discriminant analysis (DA) are the two classifiers used in this paper and the accuracy of the results is compared for the two methods of analysis. The best models were computed as a function of the grapevine variety considered. The SPA technique performed better in general with respect to common VIs, the overall classification accuracy was more than 96%. Results demonstrated that employing a feature selection technique based on the SPA algorithm can provide a valid tool for determining the optimal bands that are sensitive to Flavescence doree grapevine disease and assist in its identification. The benefit behind the presented procedure relies on the possibility of generalizing it for other infections and stresses or even for different crops.