Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 4; p. 308
• Main Authors: Albetis, Johanna, Duthoit, Sylvie, Guttler, Fabio, Jacquin, Anne, Goulard, Michel, Poilvé, Hervé, Féret, Jean-Baptiste, Dedieu, Gérard
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2017; MDPI
• Subjects: Automatic control; Band spectra; Biodiversity and Ecology; Classification; Cultivars; Detection; Diagnosis; Economics; Environmental Sciences; Feasibility studies; Ground truth; I.R. radiation; Image acquisition; Infrared imagery; Infrared signatures; Mapping; Military technology; Pest control; Plants (botany); Remote sensing; Spectral bands; Spectral signatures; Unmanned aerial vehicles; Vegetation; Vines; Vineyards; Viticulture; precision viticulture; unmanned aerial vehicle (UAV); biophysical parameters; disease detection; flavescence dorée grapevine disease; vegetation indices
• DOI: 10.3390/rs9040308
• ISSN: 2072-4292

Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed.