J. Chuche and D. Thiéry, Biology and ecology of the Flavescence dorée vector Scaphoideus titanus: A review, Agron. Sustain. Dev, vol.34, p.381, 2014.

D. Schvester, P. Carle, and G. Moutous, Sur la transmission de la Flavescence dorée des vignes par une cicadelle. Comptes Rendus des Séances de l'Académie d'Agriculture de France, vol.47, pp.1021-1024, 1961.

N. Mori, A. Bressan, M. Martin, M. Guadagnini, V. Girolami et al., Experimental transmission by Scaphoideus titanus Ball of two Flavescence doree-Type phytoplasmas, VITIS J. Grapevine Res, p.99, 2002.

J. Bonfils and D. Schvester, The leafhoppers (Homoptera: Auchenorrhyncha) and their relationship with vineyards in south-western France, Ann. Epiphyt, vol.11, pp.325-336, 1960.

A. Caudwell, Identification D'une Nouvelle Maladie à Virus de la Vigne, 1964.

F. Pavan, N. Mori, G. Bigot, and P. Zandigiacomo, Border effect in spatial distribution of Flavescence dorée affected grapevines and outside source of Scaphoideus titanus vectors, Bull. Insectol, vol.65, pp.281-290, 2012.

, Jaunisses à phytoplasmes de la vigne, p.30, 2006.

. Gdon, Guide flavescence-Aide au diagnostic de la Flavescence dorée GDON du sauternais et des Graves, Technical Report, 2014.

S. J. Kazmi and E. L. Usery, Application of remote sensing and GIS for the monitoring of diseases: A unique research agenda for geographers, Remote Sens. Rev, vol.20, pp.45-70, 2001.

J. Franke and G. Menz, Multi-temporal wheat disease detection by multi-spectral remote sensing, Precis. Agric, vol.8, pp.161-172, 2007.

S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, A review of advanced techniques for detecting plant diseases, Comput. Electron. Agric, vol.72, pp.1-13, 2010.

S. F. Gennaro, E. Battiston, S. D. Marco, O. Facini, A. Matese et al., Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex, Phytopathol. Mediterr, vol.55, pp.262-275, 2016.

G. A. Carter and A. K. Knapp, Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration, Am. J. Bot, vol.88, pp.677-684, 2001.

R. A. Naidu, E. M. Perry, F. J. Pierce, and T. Mekuria, The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars, Comput. Electron. Agric, vol.66, pp.38-45, 2009.

A. K. Mahlein, E. C. Oerke, U. Steiner, and H. W. Dehne, Recent advances in sensing plant diseases for precision crop protection, Eur. J. Plant. Pathol, vol.133, pp.197-209, 2012.

C. M. Yang, C. H. Cheng, and R. K. Chen, Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder, Crop Sci, vol.47, pp.329-335, 2007.

L. F. Johnson, D. Roczen, and S. Youkhana, Vineyard canopy density mapping with IKONOS satellite imagery, Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry, pp.5-7, 2001.

M. Mirik, D. Jones, and J. A. Price, Satellite Remote sensing of wheat infected by wheat streak mosaic virus, Plant Dis, vol.95, pp.4-12, 2011.

W. Huang, D. W. Lamb, Z. Niu, Y. Zhang, L. Liu et al., Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging, Precis. Agric, vol.8, pp.187-197, 2007.

G. J. Reynolds, C. E. Windels, I. V. Macrae, and S. Laguette, Remote sensing for assessing Rhizoctonia crown and root rot severity in sugar beet, Plant Dis, vol.96, pp.497-505, 2012.

A. R. Stilwell, G. L. Hein, A. I. Zygielbaum, and D. C. Rundquist, Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses, Int. J. Remote Sens, vol.34, pp.4951-4966, 2013.

M. Zhang, Z. Qin, X. Liu, and S. L. Ustin, Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing, Int. J. Appl. Earth Obs. Geoinf, vol.4, pp.295-310, 2003.

S. L. Macdonald, M. Staid, M. Staid, and M. L. Cooper, Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards, Comput. Electron. Agric, vol.130, pp.109-117, 2016.

F. Mazzetto, A. Calcante, A. Mena, and A. Vercesi, Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture, Precis. Agric, vol.11, pp.636-649, 2010.

I. Colomina and P. Molina, Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm. Remote Sens, vol.92, pp.79-97, 2014.

A. Matese, P. Toscano, S. F. Di-gennaro, L. Genesio, F. P. Vaccari et al., Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens, vol.7, pp.2971-2990, 2015.

C. H. Bock, F. W. Nutter, and . Jr, Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis, CAB Rev, vol.6, pp.1-15, 2011.

M. R. Steele, A. A. Gitelson, D. C. Rundquist, and M. N. Merzlyak, Nondestructive estimation of anthocyanin content in grapevine leaves, Am. J. Enol. Viticult, vol.60, pp.87-92, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01564881

M. R. Steele, A. A. Gitelson, and D. C. Rundquist, A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves, Agron. J, vol.100, pp.779-782, 2008.

A. Blondlot, P. Gate, and H. Poilvé, Providing operational nitrogen recommendations to farmers using satellite imagery, Proceedings of the 5th European Conference on Precision Agriculture, pp.345-352, 2005.

R. Lacaze, F. Baret, F. Camacho, R. Andrimont, S. C. Freitas et al., Geoland2-Towards an operational GMES land monitoring core service: The biogeophysical parameter core mapping service, Proceedings of the 34th International Symposium on Remote Sensing of Environment, pp.10-14, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01318526

F. Chen, K. T. Weber, J. Anderson, and B. Gokhale, Comparison of MODIS fPAR products with Landsat-5 TM-derived fPAR over semiarid rangelands of Idaho. GISci. Remote Sens, vol.47, pp.360-378, 2010.

M. Meroni, F. Rembold, M. M. Verstraete, R. Gommes, A. Schucknecht et al., Investigating the relationship between the inter-annual variability of satellite-derived vegetation phenology and a proxy of biomass production in the Sahel, Remote Sens, vol.6, pp.5868-5884, 2014.

A. Roumiguié, A. Jacquin, G. Sigel, H. Poilvé, O. Hagolle et al., Validation of a forage production index (FPI) derived from MODIS fCover time-series using high-resolution satellite imagery: Methodology, results and opportunities. Remote Sens, vol.7, pp.11525-11550, 2015.

H. Poilvé and . Biopar, Methods Compendium MERIS FR Biophysical Products

. Vito:-toulouse and . France, , 2010.

S. Jacquemoud, W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-tejada et al., PROSPECT+ SAIL models: A review of use for vegetation characterization, Remote Sens. Environ, vol.113, pp.56-66, 2009.

S. Jacquemoud and F. Baret, PROSPECT: A model of leaf optical properties spectra, Remote Sens. Environ, vol.34, pp.75-91, 1990.

J. B. Féret, C. François, G. P. Asner, A. A. Gitelson, R. E. Martin et al., PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments, Remote Sens. Environ, vol.112, pp.3030-3043, 2008.

J. B. Féret, S. Noble, A. Gitelson, and S. Jacquemoud, PROSPECT-Dynamic: Modeling leaf optical properties through a complete lifecycle. Remote Sens, vol.193, pp.204-215, 2017.

W. Verhoef, Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sens. Environ, vol.16, pp.125-141, 1984.

J. Rouse, . Jr, R. H. Haas, J. A. Schell, and D. W. Deering, Monitoring vegetation systems in the Great Plains with ERTS, Proceedings of the NASA Goddard Space Flight Center 3d ERTS-1 Symposium, pp.10-14, 1973.

A. A. Gitelson, M. N. Merzlyak, and O. B. Chivkunova, Optical properties and nondestructive estimation of anthocyanin content in plant leaves, Photochem. Photobiol, vol.74, pp.38-45, 2001.

A. A. Gitelson, G. P. Keydan, and M. N. Merzlyak, Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophys. Res. Lett, p.11402, 2006.

J. A. Gamon and J. S. Surfus, Assessing leaf pigment content and activity with a reflectometer, New Phytol, vol.143, pp.105-117, 1999.

A. K. Van-den-berg and T. D. Perkins, Nondestructive estimation of anthocyanin content in autumn sugar maple leaves, HortScience, vol.40, pp.685-686, 2005.

A. A. Gitelson, Y. J. Kaufman, R. Stark, and D. Rundquist, Novel algorithms for remote estimation of vegetation fraction, Remote Sens. Environ, vol.80, pp.76-87, 2002.

T. Motohka, K. N. Nasahara, H. Oguma, and S. Tsuchida, Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens, vol.2, pp.2369-2387, 2010.

A. R. Huete, A soil-adjusted vegetation index (SAVI). Remote Sens. Environ, vol.25, pp.295-309, 1988.

A. A. Gitelson, Y. J. Kaufman, and M. Merzlyak, Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ, vol.58, pp.289-298, 1996.

A. J. Richardson and C. L. Wiegand, Distinguishing vegetation from soil background information

. Photogramm and . Eng, Remote Sens, vol.43, pp.1541-1552, 1977.

C. J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ, vol.8, pp.127-150, 1979.

C. Huang, L. S. Davis, and J. R. Townshend, An assessment of support vector machines for land cover classification, Int. J. Remote Sens, vol.23, pp.725-749, 2002.

S. Saatchi, W. Buermann, H. Ter-steege, S. Mori, and T. B. Smith, Modeling distribution of Amazonian tree species and diversity using remote sensing measurements, Remote Sens. Environ, vol.112, 2000.

H. Akaike, A new look at the statistical model identification, IEEE Trans. Autom. Control, vol.19, pp.716-723, 1974.

D. Collett, Modelling Binary Data, 1991.

G. A. Blackburn and C. M. Steele, Towards the remote sensing of matorral vegetation physiology: Relationships between spectral reflectance, pigment, and biophysical characteristics of semiarid bushland canopies. Remote Sens. Environ, Licensee MDPI, vol.70, pp.278-292, 1999.

J. Chuche and D. Thiéry, Biology and ecology of the Flavescence dorée vector Scaphoideus titanus: a review

, Agronomy for sustainable development, vol.34, pp.381-403, 2014.

E. Bruez, P. Lecomte, J. Grosman, B. Doublet, C. Bertsch et al., , vol.422

J. P. Da-costa, Guerin-Dubrana, L.; others. Overview of grapevine trunk diseases in France in the, 2000.

, Phytopathologia Mediterranea, pp.262-275, 2013.

F. Fontaine, D. Gramaje, J. Armengol, R. Smart, Z. A. Nagy et al.,

, Grapevine trunk diseases. A review; Cahiers de recherche, 2016.

D. Schvester, P. Carle, and G. Moutous, Transmission de la flavescence dorée de la vigne par Scaphoideus 427 littoralis Ball, Annales des Epiphyties, vol.14, pp.175-198, 1963.

N. Mori, A. Bressan, M. Martin, M. Guadagnini, V. Girolami et al., Experimental transmission 429 by Scaphoideus titanus Ball of two Flavescence doree-type phytoplasmas, VITIS -Journal of Grapevine, vol.430, p.99, 2002.

P. Galet, Les maladies et les parasites de la vigne Tome 1

, Tec & Doc Distribution, 1999.

R. Bovey, Maladies à virus et affections similaires de la vigne

. Payot, , 1980.

J. Chuche, Comportement de Scaphoideus titanus, conséquences spatiales et démographiques, p.434

C. Pueyo, J. Carrara, and E. Parent, Flavescence dorée en Languedoc Roussillon: Bilan de 10 années de lutte 436 (Synthese des données, vol.437, p.10, 1997.

M. Vitali, W. Chitarra, L. Galetto, D. Bosco, C. Marzachì et al., Flavescence 438 dorée phytoplasma deregulates stomatal control of photosynthesis in Vitis vinifera, Annals of Applied 439 Biology, vol.162, p.11, 2013.

L. Mugnai, A. Graniti, and G. Surico, Esca (black measles) and brown wood-streaking: two old and elusive 441 diseases of grapevines, Plant disease, vol.83, pp.404-418, 1999.

L. Guerin-dubrana, A. Labenne, J. C. Labrousse, S. Bastien, P. Rey et al., Statistical analysis of 443 grapevine mortality associated with esca or Eutypa dieback foliar expression, Phytopathologia Mediterranea, vol.444, issue.2012, pp.276-288

, Journal Not Specified, vol.22, p.29, 2018.

A. M. Denizot and P. Larignon, Description des symptômes des maladies du bois -Black Dead Arm, vol.446, 2008.

A. M. Denizot and P. Larignon, Description des symptômes des maladies du bois -ESCA, vol.447, p.15, 2008.

. Maaf, Rapport annuel de la Surveillance biologique du territoire de l'année 2013, p.448

, Ministère de l'Agriculture de l'Agroalimentaire et de la Forêt, vol.449, p.16, 2013.

. Maaf, Rapport annuel de la Surveillance biologique du territoire de l'année 2015, p.450

, Ministère de l'Agriculture de l'Agroalimentaire et de la Forêt, vol.451, p.17, 2015.

S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, A review of advanced techniques for detecting plant diseases

, Computers and Electronics in Agriculture, vol.72, p.18, 2010.

F. Martinelli, R. Scalenghe, S. Davino, S. Panno, G. Scuderi et al., , p.454

M. Goulart, L. R. Davis, C. E. Dandekar, and A. M. , Advanced methods of plant disease detection. A review
URL : https://hal.archives-ouvertes.fr/hal-01284270

, Agronomy for Sustainable Development, vol.35, pp.1-25, 2015.

C. M. Yang, C. H. Cheng, and R. K. Chen, Changes in spectral characteristics of rice canopy infested with brown 457 planthopper and leaffolder, Crop science, vol.47, pp.329-335, 2007.

R. A. Naidu, E. M. Perry, F. J. Pierce, and T. Mekuria, The potential of spectral reflectance technique for the 459 detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and 460 Electronics in Agriculture, vol.66, p.21, 2009.

M. Meroni, M. Rossini, and R. Colombo, Characterization of leaf physiology using reflectance and fluorescence 462 hyperspectral measurements. Optical observation of vegetation properties and characteristics, Research Signpost, pp.165-187, 2010.

A. K. Mahlein, U. Steiner, C. Hillnhütter, H. W. Dehne, and E. C. Oerke, Hyperspectral imaging for 465 small-scale analysis of symptoms caused by different sugar beet diseases, Plant Methods, vol.8, 2012.

A. K. Mahlein, Plant Disease Detection by Imaging Sensors -Parallels and Specific Demands for Precision 468

P. Agriculture and . Phenotyping, Plant Disease, vol.100, p.24, 2015.

M. Zhang, Z. Qin, X. Liu, and S. L. Ustin, Detection of stress in tomatoes induced by late blight, p.470

U. California, using hyperspectral remote sensing, International Journal of Applied Earth Observation and 471 Geoinformation, vol.4, pp.295-310, 2003.

A. K. Mahlein, T. Rumpf, P. Welke, L. Plümer, U. Steiner et al., Development of spectral indices for 473 detecting and identifying plant diseases. Remote Sensing of Environment, vol.128, p.26, 2013.

M. R. Steele, A. A. Gitelson, D. C. Rundquist, and M. N. Merzlyak, Nondestructive estimation of anthocyanin 475 content in grapevine leaves, American Journal of Enology and Viticulture, vol.60, p.27, 2009.

J. A. Gamon and J. S. Surfus, Assessing leaf pigment content and activity with a reflectometer, New Phytologist, vol.477, p.28, 1999.

A. Hall, D. Lamb, B. Holzapfel, and J. Louis, Optical remote sensing applications in viticulture -a review, p.479

B. Lobitz, L. Johnson, C. Hlavka, R. Armstrong, and C. Bell, Grapevine Remote Sensing Analysis of Phylloxera 481

, Early Stress (GRAPES): Remote Sensing Analysis Summary, vol.483, p.30, 1997.

S. L. Macdonald, M. Staid, M. Staid, and M. L. Cooper, Remote hyperspectral imaging of grapevine 484 leafroll-associated virus 3 in cabernet sauvignon vineyards, Computers and Electronics in Agriculture, vol.485, p.31, 2016.

S. F. Gennaro, E. Battiston, S. D. Marco, O. Facini, A. Matese et al., , p.487

L. Mugnai, Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe 488 disease within a vineyard affected by esca complex, Phytopathologia Mediterranea, vol.55, pp.262-275, 2016.

M. Paindavoine, P. Zunino, F. Brossaud, and F. Cointault, Détection de foyers infectieux de Flavescence Dorée 491 par imagerie de drone, vol.492, p.33, 2015.

H. Al-saddik, J. C. Simon, O. Brousse, E. Zunino, C. Trarieux et al., Solution de 493 détection des maladies de la vigne par imagerie drone. Diagnostic et réduction des pesticides à la parcelle

, Revue des oenologues et des techniques vitivinicoles et oenologiques 2017, vol.162, p.34

J. Albetis, S. Duthoit, F. Guttler, A. Jacquin, M. Goulard et al., Detection 496 of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

, Remote Sensing, vol.9, p.308, 2017.

D. Schvester, P. Carle, and G. Moutous, « Transmission de la flavescence dorée de la vigne par Scaphoideus littoralis Ball, Annales des Epiphyties, vol.14, pp.175-198, 1963.

A. Caudwell and C. Kuszala, « Mise au point d'un test ELISA sur les tissus de vignes atteintes de flavescence dorée, Res. Microbiol, vol.143, issue.8, pp.791-806, 1992.

P. A. Bianco, A. Alma, P. Casati, G. Scattini, and A. Arzone, Transmission of 16Srv phytoplasmas by Scaphoideus titanus Ball in northern Italy, vol.37, pp.49-56, 2001.

N. Mori, A. Bressan, M. Martin, M. Guadagnini, V. Girolami et al., « Experimental transmission by Scaphoideus titanus Ball of two Flavescence doree-type phytoplasmas, VITIS -J. Grapevine Res, vol.41, issue.2, p.99, 2002.

J. Chuche and D. Thiéry, Biology and ecology of the Flavescence dorée vector Scaphoideus titanus: a review, vol.34, pp.381-403, 2014.

G. Arnaud, « Multilocus Sequence Typing Confirms the Close Genetic Interrelatedness of Three Distinct Flavescence Dorée Phytoplasma Strain Clusters and Group 16SrV Phytoplasmas Infecting Grapevine and Alder in Europe, Appl. Environ. Microbiol, vol.73, pp.4001-4010, 2007.

S. Malembic-maher, A. Arricau-bouvery, P. Carle, S. Eveillard, and X. Foissac, Dix années de recherche sur la Flavescence dorée de la vigne. » UMR 1332 Biologie du Fruit et Pathologie -INRA, 2012.

M. Maixner, W. Reinert, and H. Darimont, Transmission of grapevine yellows by Oncopsis alni (Schrank)(Auchenorrhyncha: Macropsinae) », Vitis, vol.39, pp.83-84, 2000.

L. Filippin, « Molecular characteristics of phytoplasmas associated with Flavescence dorée in clematis and grapevine and preliminary results on the role of Dictyophara europaea as a vector, Plant Pathol, vol.58, issue.5, pp.826-837, 2009.

A. , Deux années d'études sur la flavescence dorée nouvelle maladie grave de la vigne, 1957.

G. Belli, P. A. Bianco, and M. Conti, Grapevine yellows in Italy: past, present and future, J. Plant Pathol, pp.303-326, 2010.

A. , « Identification d'une nouvelle maladie à virus de la vigne," la flavescence dorée", étude des phénomènes de localisation des symptômes et de rétablissement, 1964.

P. Galet, Les Maladies et les Parasites de la Vigne : Les maladies dues à des végétaux (champignons, bactéries, viroses et phanérogames). Montpellier: Paysan du midi, 1977.

F. Lessio, A. Portaluri, F. Paparella, and E. A. Alma, A mathematical model of flavescence dorée epidemiology, vol.312, pp.41-53, 2015.

D. Papura, « Comparing the spatial genetic structures of the Flavescence dorée phytoplasma and its leafhopper vector Scaphoideus titanus, Infect. Genet. Evol, vol.9, issue.5, pp.867-876, 2009.

S. Bertin, C. R. Guglielmino, N. Karam, L. M. Gomulski, A. R. Malacrida et al., « Diffusion of the Nearctic leafhopper Scaphoideus titanus Ball in Europe: a consequence of human trading activity, Genetica, vol.131, issue.3, pp.275-285, 2007.

R. Steffek, H. Reisenzein, and N. Zeisner, « Analysis of the pest risk from grapevine flavescence dorée phytoplasma to Austrian viticulture, Blackwell Oxf. Roy.-UNI, vol.37, pp.191-203, 1970.

J. Chuche, Comportement de Scaphoideus titanus, conséquences spatiales et démographiques », Université Victor Segalen Bordeaux, vol.2, 2010.

P. Savarit, « Flavescence dorée de la vigne -Bilan National et Régional », présenté à Millésime Bio, 2015.

«. Ifv, . Flavescence-dorée--principaux, and . Enjeux, , 2017.

W. G. Weisburg, « A phylogenetic analysis of the mycoplasmas: basis for their classification, J. Bacteriol, vol.171, pp.6455-6467, 1989.

I. M. Lee, R. E. Davis, D. E. Gundersen-rindal, and «. Phytoplasma, Annu. Rev. Microbiol, vol.54, pp.221-255, 2000.

G. Firrao, K. Gibb, and C. Streten, « Short taxonomic guide to the genus'Candidatus Phytoplasma' », J. Plant Pathol, pp.249-263, 2005.

E. Angelini, E. Negrisolo, D. Clair, and M. Borgo, Boudon-Padieu, « Phylogenetic relationships among Flavescence dorée strains and related phytoplasmas determined by heteroduplex mobility assay and sequence of ribosomal and nonribosomal DNA, Plant Pathol, vol.52, issue.5, pp.663-672, 2003.

S. Malembic-maher, P. Salar, L. Filippin, P. Carle, E. Angelini et al., Candidatus Phytoplasma rubi" », Genetic diversity of European phytoplasmas of the 16SrV taxonomic group and proposal of, vol.61, pp.2129-2134, 2011.

P. Carle and G. Moutous, « Observations sur le mode de nutrition sur vigne de quatre espèces de cicadelles, Ann Epiphyt, vol.16, pp.333-354, 1965.

D. Schvester, G. Moutous, and P. Carle, « Scaphoideus littoralis Ball (Homopt. Jassidae) cicadelle vectrice de la Flavescence dorée de la vigne, Rev. Zool. Agric. Appliquée, vol.61, pp.118-131, 1962.

D. Schvester, P. Carle, and G. Moutous, « Nouvelles données sur la transmission de la flavescence dorée de la vigne par Scaphoideus littoralis Ball, Ann Zool Ecol Anim, vol.1, issue.4, pp.445-465, 1969.

E. Boudon-padieu, « Recent advances on grapevine yellows: detection, etiology, epidemiology and control strategies, Extended Abstracts 13th Meeting of ICGV, pp.87-88, 2000.

L. Beanland, R. Noble, and T. K. Wolf, « Spatial and temporal distribution of North American grapevine yellows disease and of potential vectors of the causal phytoplasmas in Virginia, Environ. Entomol, vol.35, issue.2, pp.332-344, 2006.

A. Caudwell, C. Kuszala, J. C. Bachelier, and J. Larrue, « Transmission de la Flavescence doree de la vigne aux plantes herbacees par l'allongement du temps d'utilisation de la cicadelle Scaphoideus littoralis Ball et l'etude de sa survie sur en grand nombre d'especes vegetales, 1970.

N. M. Christensen, K. B. Axelsen, M. Nicolaisen, and A. Schulz, Phytoplasmas and their interactions with hosts, vol.10, pp.526-535, 2005.

M. Bertamini and N. Nedunchezhian, on Photosynthetic Pigments, Saccharides, Ribulose 1,5-Bisphosphate Carboxylase, Nitrate and Nitrite Reductases, and Photosynthetic Activities in FieldGrown Grapevine, Effects of Phytoplasma, vol.39, pp.119-122, 2001.

P. Galet, Les maladies et les parasites de la vigne Tome 1, Tec & Doc Distribution, 1999.

R. Bovey, Maladies à virus et affections similaires de la vigne, 1980.

«. Gtn, Jaunisses à phytoplasmes de la vigne, Groupe de travail national Flavescence dorée, 2006.

W. A. Hoch, E. L. Singsaas, and B. H. Mccown, « Resorption protection. Anthocyanins facilitate nutrient recovery in autumn by shielding leaves from potentially damaging light levels, Plant Physiol, vol.133, issue.3, pp.1296-1305, 2003.

M. Bertamini, N. Nedunchezhian, F. Tomasi, M. S. Grando, and . Phytoplasma, Stolbursubgroup (Bois Noir-BN)] infection inhibits photosynthetic pigments, ribulose-1,5-bisphosphate carboxylase and photosynthetic activities in field grown grapevine (Vitis vinifera L. cv. Chardonnay) leaves, Physiol. Mol. Plant Pathol, vol.61, issue.6, pp.357-366, 2002.

P. Margaria, A. Ferrandino, P. Caciagli, O. Kedrina, A. Schubert et al., « Metabolic and transcript analysis of the flavonoid pathway in diseased and recovered Nebbiolo and Barbera grapevines (Vitis vinifera L.) following infection by Flavescence dorée phytoplasma, Plant Cell Environ, vol.37, issue.9, pp.2183-2200, 2014.

J. Féret, Apport de la modélisation pour l'estimation de la teneur en pigments foliaires par télédétection, 2009.

C. Pueyo, J. Carrara, E. Parent, and . Flavescence-dorée-en-languedoc-roussillon, Bilan de 10 années de lutte (Synthese des données 1997 -2007), 2008.

M. Vitali, « Flavescence dorée phytoplasma deregulates stomatal control of photosynthesis in Vitis vinifera, Ann. Appl. Biol, vol.162, issue.3, pp.335-346, 2013.

A. Alma, G. Soldi, R. Tedeschi, and C. Marzachì, « Ruolo di Hyalesthes obsoletus Signoret (Homoptera, Cixiidae) nella trasmissione del Legno nero della vite in Italia, Petria, vol.12, issue.3, pp.411-412, 2002.

A. Bressan, R. Turata, M. Maixner, S. Spiazzi, E. Boudon-padieu et al., Vector activity of Hyalesthes obsoletus living on nettles and transmitting a stolbur phytoplasma to grapevines: a case study, vol.150, pp.331-339, 2007.

A. Denizot and P. Larignon, Description des symptômes des maladies du bois -Black Dead Arm ». IFV -Institut Francais de la Vigne et du Vin, 2008.

A. Denizot and P. Larignon, Description des symptômes des maladies du bois -ESCA ». IFV -Institut Francais de la Vigne et du Vin, 2008.

M. Hren, J. Boben, A. Rotter, P. Kralj, K. Gruden et al., « Real-time PCR detection systems for Flavescence dorée and Bois noir phytoplasmas in grapevine: comparison with conventional PCR detection and application in diagnostics, Plant Pathol, vol.56, issue.5, pp.785-796, 2007.

D. Blancard, Vigne -Index des maladies, ravageurs et vecteurs, 2017.

P. Lecomte, « les maladies du bois de la vigne, 2014.

D. Sur,

«. Ifv and . Institut-français-de-la-vigne, fiches pratiques

P. Guilbault, « Identification des principales carences de la vigne, Avenir Agricole Aquitain, pp.1-9, 2003.

D. Blancard, C. Deluche, and . Vigne--phytotoxicités, , 2017.

D. Sur,

, Les pratiques de lutte contre la Flavescence Dorée en Europe, 2016.

«. Maaf, Modalités de surveillance et de lutte contre les phytoplasmes de la vigne (flavescence dorée et bois noir) », Ministère de l'Agriculture de l'Agroalimentaire et de la Forêt, 2017.

N. Constant and J. Lernould, « La gestion de la Flavescence dorée en viticulture biologique, 2014.

P. Salar, C. Charenton, X. Foissac, and S. Malembic-maher, « Multiplication kinetics of Flavescence dorée phytoplasma in broad bean. Effect of phytoplasma strain and temperature », Eur. J. Plant Pathol, vol.135, issue.2, pp.371-381, 2013.

M. Kasbari and B. Leroux, « Méthodologie pour l'usage d'un drone de catégorie E pour la détection de la flavescence dorée, Cah. Tech, 2016.

«. Poissonnet, Un drone pour détecter la flavescence dorée », mon-ViTi, 2014.

D. Sur,

C. Quéré and J. M. Sermier, « Rapport d'information déposé par la commission du développement durable et de l'aménagement du territoire en application de l'article 145 du Règlement sur les maladies de la vigne et du bois, p.2946, 2015.

«. Vitisphere, /. Viticulture, and . Oenologie, , 2011.

B. Collard, /. Viticulture, and . Oenologie, Les parcelles arrachées seront primées par les aides à la restructuration, 2014.

F. Lessio, F. Tota, and E. A. Alma, « Tracking the dispersion of Scaphoideus titanus Ball (Hemiptera: Cicadellidae) from wild to cultivated grapevine: use of a novel markcapture technique, Bull. Entomol. Res, vol.104, pp.432-443, 2014.

«. Ca81 and . Flavescence, , 2015.

F. Lessio, R. Tedeschi, and E. A. Alma, Presence of Scaphoideus titanus on American grapevine in woodlands, and infection with "flavescence dorée" phytoplasmas, vol.60, pp.373-374, 2007.

«. Inra and . Projet, Fladorisk : flavescence dorée de la vigne : mesurer l'influence de l'environnement « sauvage » des vignobles, 2015.

, Ecologie et comportement de Scaphoideus titanus : rôle dans l'invasion du vignoble par la Flavescence dorée

, Istituto di Virologia Vegetale -Projects

D. Sur,

«. Euphresco, Final report -GRAFDEPI Project, 2015.

. Franceagrimer, Synthèse du projet FAM : lutte contre la cicadelle de la Flavescence Dorée avec le Pyrèthre naturel, 2017.

J. Albetis, « Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery, Remote Sens, vol.9, p.308, 2017.

H. Al-saddik, « Solution de détection des maladies de la vigne par imagerie drone. Diagnostic et réduction des pesticides à la parcelle, Rev. Oenologues Tech. Vitivinic. Oenologiques, vol.162, pp.14-15, 2017.

C. Pelletier, « Triplex real-time PCR assay for sensitive and simultaneous detection of grapevine phytoplasmas of the 16SrV and 16SrXII-A groups with an endogenous analytical control, vol.48, pp.87-95, 2009.

«. Avidordrone, Identification des maladies de la vigne -Avidordrone

D. Sur,

A. Laurie, « Carbon Bee : un détecteur de maladies et stress de végétaux », L'agriculture Drômoise, 2016.

D. Sur, carbon-bee-un-detecteur-de-maladies-et-stress-devegetaux, vol.11, 2016.

«. Ifv, . Winetwork-european, and . Knowledgetransfer, , 2015.

A. Mahlein, « Plant Disease Detection by Imaging Sensors -Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping, Plant Dis, vol.100, issue.2, pp.241-251, 2015.

S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, « A review of advanced techniques for detecting plant diseases, Comput. Electron. Agric, vol.72, issue.1, pp.1-13, 2010.

J. J. Belasque, M. C. Gasparoto, and L. G. Marcassa, « Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy, Appl. Opt, vol.47, issue.11, pp.1922-1926, 2008.

C. Collet, Précis de télédétection: traitements numériques d'images de télédétection, 2001.

R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti et al., Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal viewangle range to increase the sensitivity, Comput. Electron. Agric, vol.104, pp.1-8, 2014.

R. G. Bramley, « Progress in the development of precision viticulture-variation in yield, quality and soil properties in contrasting Australian vineyards, 2001.

G. A. Carter and A. K. Knapp, « Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration », Am. J. Bot, vol.88, issue.4, pp.677-684, 2001.

D. A. Sims and J. A. Gamon, « Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages, Remote Sens. Environ, vol.81, issue.2-3, pp.337-354, 2002.

H. Mestre, The absorption of radiation by leaves and algae, Cold Spring Harbor symposia on quantitative biology, vol.3, pp.191-209, 1935.

E. B. Knipling, « Physical and physiological basis for the reflectance of visible and nearinfrared radiation from vegetation, Remote Sens. Environ, vol.1, issue.3, pp.155-159, 1970.

W. A. Allen, H. W. Gausman, A. J. Richardson, and J. R. Thomas, JOSA, vol.59, issue.10, pp.1376-1379, 1969.

H. Al-saddik, J. Simon, and F. Cointault, « Development of Spectral Disease Indices for "Flavescence Dorée, Grapevine Disease Identification », Sensors, vol.17, 2017.

F. Guttler, S. Duthoit, M. Fauvel, and A. Jacquin, « Spectral analysis of Vitis vinifera leaves for the detection of the Flavescence dorée disease in red and white cultivars, 2018.

N. Vigneau, Potentiel de l'imagerie hyperspectrale de proximité comme outil de phénotypage: application à la concentration en azote du blé, 2010.

G. V. Baranoski and J. , Rokne, « A practical approach for estimating the red edge position of plant leaf reflectance, Int. J. Remote Sens, vol.26, issue.3, pp.503-521, 2005.

J. S. West, G. G. Canning, S. A. Perryman, and E. K. King, « Novel Technologies for the detection of Fusarium head blight disease and airborne inoculum, Trop. Plant Pathol, vol.42, issue.3, pp.203-209, 2017.

A. Mahlein, E. Oerke, U. Steiner, and H. Dehne, « Recent advances in sensing plant diseases for precision crop protection », Eur J Plant Pathol, vol.133, pp.197-209, 2012.

I. Dhau, « Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop, Geocarto Int, pp.1-28, 2017.

S. L. Macdonald, M. Staid, M. Staid, and M. L. Cooper, « Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards, Comput. Electron. Agric, vol.130, pp.109-117, 2016.

S. F. Gennaro, « Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex, Phytopathol. Mediterr, vol.55, issue.2, pp.262-275, 2016.

L. Yuan, R. Pu, J. Zhang, J. Wang, and H. Yang, Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale, Precis. Agric, vol.17, issue.3, pp.332-348, 2016.

B. Lobitz, L. Johnson, C. Hlavka, R. Armstrong, and C. Bell, Grapevine Remote Sensing Analysis of Phylloxera Early Stress (GRAPES): Remote Sensing Analysis Summary, 1997.

C. Hillnhütter, A. Mahlein, R. Sikora, and E. Oerke, « Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields, Field Crops Res, vol.122, pp.70-77, 2011.

A. Patrick, S. Pelham, A. Culbreath, C. C. Holbrook, I. J. Godoy et al., High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging, IEEE Instrum. Meas. Mag, vol.20, issue.3, pp.4-12, 2017.

C. Yang, G. N. Odvody, J. A. Thomasson, T. Isakeit, and R. L. Nichols, « Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery, Comput. Electron. Agric, vol.123, pp.154-162, 2016.

Z. Qin and M. Zhang, « Detection of rice sheath blight for in-season disease management using multispectral remote sensing », Int. J. Appl. Earth Obs. Geoinformation, vol.7, pp.115-128, 2005.

J. Franke and G. Menz, « Multi-temporal wheat disease detection by multi-spectral remote sensing, Precis. Agric, vol.8, issue.3, pp.161-172, 2007.

M. Mirik, D. C. Jones, and J. A. Price, « Satellite Remote Sensing of Wheat Infected by Wheat streak mosaic virus, Plant Dis, vol.95, issue.1, pp.4-12, 2011.

T. Mewes, J. Franke, and G. Menz, « Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection, Precis. Agric, vol.12, issue.6, p.795, 2011.

A. Mahlein, U. Steiner, H. Dehne, and E. Oerke, « Spectral signatures of sugar beet leaves for the detection and differentiation of diseases, Precis. Agric, vol.11, pp.413-431, 2010.

D. Cui, Q. Zhang, M. Li, Y. Zhao, and G. L. Hartman, « Detection of soybean rust using a multispectral image sensor, Sens. Instrum. Food Qual. Saf, vol.3, issue.1, pp.49-56, 2009.

C. Yang, C. Cheng, and R. Chen, « Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder, Crop Sci, vol.47, issue.1, pp.329-335, 2007.

R. A. Naidu, E. M. Perry, F. J. Pierce, and T. Mekuria, « The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars, Comput. Electron. Agric, vol.66, issue.1, pp.38-45, 2009.

M. R. Steele, A. A. Gitelson, and D. C. Rundquist, « A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves, Agron. J, vol.100, issue.3, pp.779-782, 2008.

A. K. Van-den, T. D. Berg, and . Perkins, « Nondestructive estimation of anthocyanin content in autumn sugar maple leaves, HortScience, vol.40, issue.3, pp.685-686, 2005.

M. R. Steele, A. A. Gitelson, D. C. Rundquist, and M. N. Merzlyak, « Nondestructive estimation of anthocyanin content in grapevine leaves, Am. J. Enol. Vitic, vol.60, issue.1, pp.87-92, 2009.

J. A. Gamon and J. S. Surfus, « Assessing leaf pigment content and activity with a reflectometer, New Phytol, vol.143, issue.1, pp.105-117, 1999.

S. Sankaran, R. Ehsani, S. A. Inch, and R. C. Ploetz, « Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt, Plant Dis, vol.96, issue.11, pp.1683-1689, 2012.

H. Z. Shafr and N. Hamdan, « Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques », Am. J. Appl. Sci, vol.6, issue.6, pp.1031-1035, 2009.

L. Bronge, « Satellite remote sensing for estimating leaf area index, FPAR and primary production. A literature review », Swedish Nuclear Fuel and Waste Management Co, 2004.

T. Dahms, S. Seissiger, C. Conrad, and E. E. Borg, « Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series, ISPRS -Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, vol.49, issue.2, pp.171-175, 2016.

H. Aitouda, Estimation des variables biophysiques des cultures et étude de l'effet du changement d'échelle sur leur variabilité spatiale, 2012.

A. Blondlot, P. Gate, and H. Poilvé, « Providing operational nitrogen recommendations to farmers using satellite imagery, Proceedings of the 5th European Conference on Precision Agriculture, pp.345-352, 2005.

R. Lacaze, « geoland2-towards an operational GMES land monitoring core service: the biogeophysical parameter core mapping service, Proceedings of the 34th International Symposium on Remote Sensing of Environment, 2011.

A. Roumiguié, A. Jacquin, G. Sigel, H. Poilvé, O. Hagolle et al., « Validation of a forage production index (FPI) derived from MODIS fCover time-series using highimagery: methodology, results and opportunities, Remote Sens, vol.7, issue.9, pp.11525-11550, 2015.

A. P. Fossi, « Miniaturisation d'une caméra hyperspectrale infrarouge », phdthesis, 2016.

H. Delacour, A. Servonnet, A. Perrot, J. F. Vigezzi, J. M. Ramirez et al., Receiver operating characteristic) : principes et principales applications en biologie clinique, Ann. Biol. Clin, vol.63, issue.2, pp.145-154, 2005.

«. Ivso, ». Aop-gaillac, L. Vins, and . Ouest--france,

, Disponible sur: /fr/denomination/aop-gaillac

P. Courjault-radé, P. Munoz, and N. Hirissou, Incidences sur la détermination des unités terroir de base et sur le choix du matériel végétal, Caractérisation de la composante géologique de parcelles du vignoble de Gaillac, vol.39, pp.95-107, 2005.

«. Mvv, . Bulletin-flavescence, and . Dorée, Maison de la Vigne et du Vin de Gaillac, 2015.

C. Pueyo, Flavescence dorée de la vigne -Projet d'arrêté préfectoral Occitanie », SRAL Occitanie, 2017.

J. Albetis, « Demande d'information sur la présence des maladies de vigne dans l'AOC Gaillac, Destinataire : Virgine VIGUES, pp.6-2017

«. Civl, ». Aoc-minervois-la-livinière, and L. Wines, , 2017.

D. Sur,

, Cahier des charges de l'appellation d'origine contrôlée "Minervois-La-Livinière" ». JORF, 2011.

«. Cr, La flavescence dorée sévit en en Languedoc Roussillon -Coordination Rurale (CR) », Coordination Rurale (CR), 2014.

J. Albetis, « Demande d'information sur la présence des maladies de vigne dans l'AOC Minervois-La Livinière, Destinataire : Jacques ROUSSEAU, pp.21-2017

J. Plantes and «. Palisser-la-vigne, , 2016.

D. Sur,

J. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, « Monitoring vegetation systems in the Great Plains with ERTS, NASA Spec. Publ, vol.351, p.309, 1974.

A. A. Gitelson, Y. J. Kaufman, R. Stark, and D. Rundquist, « Novel algorithms for remote estimation of vegetation fraction, Remote Sens. Environ, vol.80, issue.1, pp.76-87, 2002.

T. Motohka, K. N. Nasahara, H. Oguma, and E. S. Tsuchida, « Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology, Remote Sens, vol.2, issue.10, pp.2369-2387, 2010.

A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sens. Environ, vol.58, issue.3, pp.289-298, 1996.

A. J. Richardson and C. L. Wiegand, « Distinguishing vegetation from soil background information, », Photogramm. Eng. Remote Sens, vol.43, pp.1541-1552, 1977.

C. J. Tucker, « Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ, vol.8, issue.2, pp.127-150, 1979.

A. R. Huete, « A soil-adjusted vegetation index (SAVI), Remote Sens. Environ, vol.25, issue.3, pp.295-309, 1988.

A. A. Gitelson, M. N. Merzlyak, and O. B. Chivkunova, « Optical properties and nondestructive estimation of anthocyanin content in plant leaves, Photochem. Photobiol, vol.74, issue.1, pp.38-45, 2001.

A. A. Gitelson, G. P. Keydan, and M. N. Merzlyak, « Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophys. Res. Lett, vol.33, issue.11, p.11402, 2006.

P. J. Zarco-tejada, J. R. Miller, T. L. Noland, G. H. Mohammed, and P. H. Sampson, « Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE Trans. Geosci. Remote Sens, vol.39, issue.7, pp.1491-1507, 2001.

J. Peñuelas, J. A. Gamon, A. L. Fredeen, J. Merino, and C. B. Field, « Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves, Remote Sens. Environ, vol.48, issue.2, pp.135-146, 1994.

A. Gitelson and M. N. Merzlyak, « Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves, J. Photochem. Photobiol. B, vol.22, issue.3, pp.247-252, 1994.

A. Gitelson and M. N. Merzlyak, « Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation, J. Plant Physiol, vol.143, issue.3, pp.286-292, 1994.

H. Poilvé, « Towards an Operational GMES Land Monitoring Core Service -BioPar Product User Manual -MERIS FR Biophysical Products, 2010.

F. X. Kneizys, E. P. Shettle, L. W. Abreu, J. H. Chetwynd, G. P. Anderson et al., Users guide to LOWTRAN 7, 1988.

S. Jacquemoud, F. Baret, and «. Prospect, A model of leaf optical properties spectra, Remote Sens. Environ, vol.34, issue.2, pp.75-91, 1990.

W. Verhoef, « Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model, Remote Sens. Environ, vol.16, issue.2, pp.125-141, 1984.

J. Féret, A. A. Gitelson, S. D. Noble, S. Jacquemoud, and «. Prospect-d, Towards modeling leaf optical properties through a complete lifecycle, Remote Sens. Environ, vol.193, pp.204-215, 2017.

J. Féret, « PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments, Remote Sens. Environ, vol.112, issue.6, pp.3030-3043, 2008.

S. Saatchi, W. Buermann, H. Steege, S. Mori, and T. B. Smith, « Modeling distribution of Amazonian tree species and diversity using remote sensing measurements, Remote Sens. Environ, vol.112, issue.5, pp.2000-2017, 2008.

T. Fawcett, « An introduction to ROC analysis, Pattern Recognit. Lett, vol.27, issue.8, pp.861-874, 2006.

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, pp.115-163, 2001.

B. L. Welch, « The generalization of "Student's" problem when several different population variances are involved, Biometrika, vol.34, issue.2, pp.28-35, 1947.

P. Mccullagh and J. A. Nelder, , 1989.

R. R. Hocking, Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression, Biometrics, vol.32, issue.1, pp.1-49, 1976.

H. Akaike, A new look at the statistical model identification, IEEE Trans. Autom. Control, vol.19, issue.6, pp.716-723, 1974.

W. Huang, D. W. Lamb, Z. Niu, Y. Zhang, L. Liu et al., « Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging, Precis. Agric, vol.8, pp.187-197, 2007.

C. Bertsch, « Grapevine trunk diseases: complex and still poorly understood, Plant Pathol, vol.62, issue.2, 2012.

L. Guerin-dubrana, A. Labenne, J. C. Labrousse, S. Bastien, P. Rey et al., « Statistical analysis of grapevine mortality associated with esca or Eutypa dieback foliar expression, Phytopathol. Mediterr, vol.52, issue.2, pp.276-288, 2012.

L. Mugnai, A. Graniti, and G. Surico, « Esca (black measles) and brown wood-streaking: two old and elusive diseases of grapevines, Plant Dis, vol.83, issue.5, pp.404-418, 1999.

D. Ashourloo, M. R. Mobasheri, and E. A. Huete, « Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements, Remote Sens, vol.6, issue.6, pp.5107-5123, 2014.

A. Hall, J. Louis, and E. D. Lamb, « Characterising and mapping vineyard canopy using highspatial-resolution aerial multispectral images, Comput. Geosci, vol.29, issue.7, pp.813-822, 2003.

C. Bravo, « Foliar Disease Detection in the Field Using Optical Sensor Fusion, 2004.

J. S. West, C. Bravo, R. Oberti, D. Lemaire, D. Moshou et al., « The potential of optical canopy measurement for targeted control of field crop diseases, Annu. Rev. Phytopathol, vol.41, pp.593-614, 2003.

T. Braun, H. Koch, O. Strub, G. Zolynski, and K. Berns, « Improving pesticide spray application in vineyards by automated analysis of the foliage distribution pattern in the leaf wall, Proceedings of the 1st Commercial Vehicle Technology Symposium, 2010.

A. J. Mathews and J. L. Jensen, Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud, Remote Sens, vol.5, issue.5, pp.2164-2183, 2013.

R. Retzlaff, « UAS-based multi-angular remote sensing of the effects of soil management strategies on grapevine, OENO One, vol.49, issue.2, p.85, 2015.

A. Hall, D. Lamb, B. Holzapfel, and J. Louis, « Optical remote sensing applications in viticulture -a review, 2008.

A. P. Nolan, S. Park, S. Fuentes, and E. D. Ryu, Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard », 21st Int, Congr. Model. Simul, p.2015

L. Comba, P. Gay, J. Primicerio, and D. Ricauda-aimonino, « Vineyard detection from unmanned aerial systems images, Comput. Electron. Agric, vol.114, pp.78-87, 2015.

C. Terrain,

, Sélection des parcelles

, et Braucol) et 3 cépages blancs (Colombard, Loin de L'oeil, Muscadelle Sauvignon), nous avons sélectionné les parcelles de manière à avoir des mesures pour une diversité de cépages. Au final, nous avons choisi 7 cépages différents dans l'AOC Gaillac : 3 cépages noirs

, nous avons sélectionné les parcelles de manière à avoir des mesures pour une diversité de cépages mais aussi pour des cépages identiques à ceux mesurés en 2015 afin d'analyser la variabilité au sein d'un même cépage. 10 parcelles au total ont été sélectionnées dans l'AOC Gaillac et l'AOC Minervois-La Livinière, dont 8 avec trois classes échantillonnées (Flavescence dorée, maladies du bois et pied sain) et les 2 autres uniquement Flavescence dorée et pied sain, Cépages blancs et noirs sélectionnées en 2015 et 2016 pour l'acquisition des données, vol.1