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Remote Sens. 2017, 9(4), 308; doi:10.3390/rs9040308

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

1
Ecole d’Ingénieurs de PURPAN, Université de Toulouse, INPT, UMR 1201 DYNAFOR, 75 voie du TOEC, BP 57611, F-31076 Toulouse Cedex 03, France
2
TerraNIS, 10 avenue de l’Europe, F-31520 Ramonville-saint-agne, France
3
INRA, UMR 1201 DYNAFOR, 24 chemin de borderouge, CS 52627, F-31326 Castanet-Tolosan Cedex, France
4
AIRBUS Defense and Space, 5 rue des satellites, F-31400 Toulouse, France
5
IRSTEA, UMR TETIS, 500 rue Jean-François Breton, F-34000 Montpellier, France
6
CESBIO, UMR 5126 CNES-UPS-CNRS-IRD, 18 avenue Edouard Belin, BPI 2801, F-31401 Toulouse Cedex 9, France
*
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Francesco Nex, Clement Atzberger and Prasad S. Thenkabail
Received: 30 December 2016 / Revised: 13 March 2017 / Accepted: 15 March 2017 / Published: 24 March 2017
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
View Full-Text   |   Download PDF [24728 KB, uploaded 24 March 2017]   |  

Abstract

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. View Full-Text
Keywords: precision viticulture; disease detection; unmanned aerial vehicle (UAV); Flavescence dorée grapevine disease; vegetation indices; biophysical parameters precision viticulture; disease detection; unmanned aerial vehicle (UAV); Flavescence dorée grapevine disease; vegetation indices; biophysical parameters
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Albetis, J.; Duthoit, S.; Guttler, F.; Jacquin, A.; Goulard, M.; Poilvé, H.; Féret, J.-B.; Dedieu, G. Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens. 2017, 9, 308.

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