Potential of multispectral images acquired by drone in the detection of areas infected by grapevine flavescence dorée.

Summary This thesis addresses the potential of remote sensing as a tool for the automatic detection of Grapevine Flavescence dorée (FD). The approach is based on the analysis of variables (spectral bands, vegetation indices and biophysical parameters) that can be calculated from very high resolution (10 cm) multispectral images acquired by drone during the period of maximum symptom expression. The analysis of the discrimination performance of the variables is performed using a supervised method based on the ROC curve. The training and validation areas used in this study were acquired on 14 plots located in the south of France. The performance of the variables was tested on three scales of analysis (by plot, by grape variety and by color) and for two levels of analysis. The first level of analysis is based on the potential of the variables used in the detection of Flavescence dorée symptomatic areas from asymptomatic areas. The second level of analysis consists in testing the performance of the variables in the specific discrimination of Flavescence dorée (black grape varieties) by making a distinction with wood diseases. At the end of these experiments, from a methodological point of view, the results showed (1) a lower discrimination performance for the discrimination of FD symptomatic areas from wood diseases symptomatic areas, especially at the color scale. (2) the presence of poorly classified mixed pixels especially in the edges of the vine rows and (3) a poor discrimination of symptomatic areas (FD or MB) with a low proportion of symptomatic foliage (infection level). From a thematic point of view, the results obtained highlighted the differences in the intensity of abnormal coloration of Flavescence dorée-infected leaves depending on the year and their link with the chlorophyll and anthocyanin content of the leaves. The perspectives opened by this work concern the creation of an index specific to Flavescence dorée according to the color of the grape variety (black or white) or the intensity of the coloration of the leaves (weak or strong) identified from hyperspectral data and the improvement of the masking of the mixed pixels using complex algorithms which take into account the spatial distribution of the pixels in the vine foliage.