{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:48:18Z","timestamp":1775198898385,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,23]],"date-time":"2018-12-23T00:00:00Z","timestamp":1545523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Among grapevine diseases affecting European vineyards, Flavescence dor\u00e9e (FD) and Grapevine Trunk Diseases (GTD) are considered the most relevant challenges for viticulture because of the damage they cause to vineyards. Unmanned Aerial Vehicle (UAV) multispectral imagery could be a powerful tool for the automatic detection of symptomatic vines. However, one major difficulty is to discriminate different kinds of diseases leading to similar leaves discoloration as it is the case with FD and GTD for red vine cultivars. The objective of this paper is to evaluate the potentiality of UAV multispectral imagery to separate: symptomatic vines including FD and GTD (Esca and black dead arm) from asymptomatic vines (Case 1) and FD vines from GTD ones (Case 2). The study sites are localized in the Gaillac and Minervois wine production regions (south of France). A set of seven vineyards covering five different red cultivars was studied. Field work was carried out between August and September 2016. In total, 218 asymptomatic vines, 502 FD vines and 199 GTD vines were located with a centimetric precision GPS. UAV multispectral images were acquired with a MicaSense RedEdge\u00ae sensor and were processed to ultimately obtain surface reflectance mosaics at 0.10 m ground spatial resolution. In this study, the potentiality of 24 variables (5 spectral bands, 15 vegetation indices and 4 biophysical parameters) are tested. The vegetation indices are selected for their potentiality to detect abnormal vegetation behavior in relation to stress or diseases. Among the biophysical parameters selected, three are directly linked to the leaf pigments content (chlorophyll, carotenoid and anthocyanin). The first step consisted in evaluating the performance of the 24 variables to separate symptomatic vine vegetation (FD or\/and GTD) from asymptomatic vine vegetation using the performance indicators from the Receiver Operator Characteristic (ROC) Curve method (i.e., Area Under Curve or AUC, sensibility and specificity). The second step consisted in mapping the symptomatic vines (FD and\/or GTD) at the scale of the field using the optimal threshold resulting from the ROC curve. Ultimately, the error between the level of infection predicted by the selected variables (proportion of symptomatic pixels by vine) and observed in the field (proportion of symptomatic leaves by vine) is calculated. The same methodology is applied to the three levels of analysis: by vineyard, by cultivar (Gamay, Fer Servadou) and by berry color (all red cultivars). At the vineyard and cultivar levels, the best variables selected varies. The AUC of the best vegetation indices and biophysical parameters varies from 0.84 to 0.95 for Case 1 and 0.74 to 0.90 for Case 2. At the berry color level, no variable is efficient in discriminating FD vines from GTD ones (Case 2). For Case 1, the best vegetation indices and biophysical parameter are Red Green Index (RGI)\/ Green-Red Vegetation Index (GRVI) (based on the green and red spectral bands) and Car (linked to carotenoid content). These variables are more effective in mapping vines with a level of infection greater than 50%. However, at the scale of the field, we observe misclassified pixels linked to the presence of mixed pixels (shade, bare soil, inter-row vegetation and vine vegetation) and other factors of abnormal coloration (e.g., apoplectic vines).<\/jats:p>","DOI":"10.3390\/rs11010023","type":"journal-article","created":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T10:37:49Z","timestamp":1545647869000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dor\u00e9e and Grapevine Trunk Diseases"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-5048","authenticated-orcid":false,"given":"Johanna","family":"Albetis","sequence":"first","affiliation":[{"name":"Ecole d\u2019Ing\u00e9nieurs de PURPAN, Universit\u00e9 de Toulouse, Toulouse INP, 75 voie du TOEC, BP 57611, F-31076 Toulouse CEDEX 3, France"}]},{"given":"Anne","family":"Jacquin","sequence":"additional","affiliation":[{"name":"AIRBUS Defense and Space, 5 rue des satellites, F-31400 Toulouse, France"}]},{"given":"Michel","family":"Goulard","sequence":"additional","affiliation":[{"name":"UMR 1201 DYNAFOR, Universit\u00e9 de Toulouse, INRA, 24 chemin de borderouge, CS 52627, F-31326 Castanet-Tolosan CEDEX, France"}]},{"given":"Herv\u00e9","family":"Poilv\u00e9","sequence":"additional","affiliation":[{"name":"AIRBUS Defense and Space, 5 rue des satellites, F-31400 Toulouse, France"}]},{"given":"Jacques","family":"Rousseau","sequence":"additional","affiliation":[{"name":"Groupe ICV, La Jasse de Maurin, F-34970 Lattes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6743-7798","authenticated-orcid":false,"given":"Harold","family":"Clenet","sequence":"additional","affiliation":[{"name":"Ecole d\u2019Ing\u00e9nieurs de PURPAN, Universit\u00e9 de Toulouse, Toulouse INP, 75 voie du TOEC, BP 57611, F-31076 Toulouse CEDEX 3, France"},{"name":"UMR 1201 DYNAFOR, Universit\u00e9 de Toulouse, INRA, 24 chemin de borderouge, CS 52627, F-31326 Castanet-Tolosan CEDEX, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8383-6465","authenticated-orcid":false,"given":"Gerard","family":"Dedieu","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, UMR 5126 CNES-UPS-CNRS-IRD, 18 avenue Edouard Belin, BPI 2801, F-31401 Toulouse CEDEX 9, France"}]},{"given":"Sylvie","family":"Duthoit","sequence":"additional","affiliation":[{"name":"TerraNIS, 12 avenue de l\u2019Europe, F-31520 Ramonville-saint-agne, France"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s13593-014-0208-7","article-title":"Biology and ecology of the Flavescence dor\u00e9e vector Scaphoideus titanus: A review","volume":"34","author":"Chuche","year":"2014","journal-title":"Agron. Sustain. Dev."},{"key":"ref_2","first-page":"262","article-title":"Overview of grapevine trunk diseases in France in the 2000s","volume":"52","author":"Bruez","year":"2013","journal-title":"Phytopathol. Mediterr."},{"key":"ref_3","unstructured":"Fontaine, F., Gramaje, D., Armengol, J., Smart, R., Nagy, Z.A., Borgo, M., Rego, C., and Corio-Costet, M.F. (2016). Grapevine Trunk Diseases. A Review, Cahiers de recherche, OIV Publications."},{"key":"ref_4","unstructured":"MAAF (2013). Rapport annuel de la Surveillance biologique du territoire de l\u2019ann\u00e9e 2013, Technical Report."},{"key":"ref_5","unstructured":"MAAF (2015). Rapport annuel de la Surveillance biologique du territoire de l\u2019ann\u00e9e 2015, Technical Report."},{"key":"ref_6","first-page":"175","article-title":"Transmission de la flavescence dor\u00e9e de la vigne par Scaphoideus littoralis Ball","volume":"14","author":"Schvester","year":"1963","journal-title":"Annales des Epiphyties"},{"key":"ref_7","first-page":"99","article-title":"Experimental transmission by Scaphoideus titanus Ball of two Flavescence doree-type phytoplasmas","volume":"41","author":"Mori","year":"2002","journal-title":"VITIS J. Grapevine Res."},{"key":"ref_8","unstructured":"Galet, P. (1999). Les maladies et les parasites de la vigne Tome 1, Tec & Doc Distribution."},{"key":"ref_9","unstructured":"Bovey, R. (1980). Maladies \u00e0 virus et affections similaires de la vigne, La Maison rustique."},{"key":"ref_10","unstructured":"Chuche, J. (2010). Comportement de Scaphoideus Titanus, Cons\u00e9Quences Spatiales et D\u00e9Mographiques. [Ph.D. Thesis, Universit\u00e9 Victor Segalen Bordeaux 2]."},{"key":"ref_11","unstructured":"Pueyo, C., Carrara, J., and Parent, E. (2008). Flavescence dor\u00e9e en Languedoc Roussillon: Bilan de 10 ann\u00e9es de lutte (Synthese des donn\u00e9es 1997\u20132007), Direction R\u00e9gionale de l\u2019Agriculture et de la For\u00eat Languedoc-Roussillon, Service R\u00e9gional de la Protection des V\u00e9g\u00e9taux."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/aab.12025","article-title":"Flavescence dor\u00e9e phytoplasma deregulates stomatal control of photosynthesis in Vitis vinifera","volume":"162","author":"Vitali","year":"2013","journal-title":"Ann. Appl. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1094\/PDIS.1999.83.5.404","article-title":"Esca (black measles) and brown wood-streaking: Two old and elusive diseases of grapevines","volume":"83","author":"Mugnai","year":"1999","journal-title":"Plant Dis."},{"key":"ref_14","first-page":"276","article-title":"Statistical analysis of grapevine mortality associated with esca or Eutypa dieback foliar expression","volume":"52","author":"Labenne","year":"2012","journal-title":"Phytopathol. Mediterr."},{"key":"ref_15","unstructured":"Denizot, A.M., and Larignon, P. (2008). Description des sympt\u00f4mes des maladies du bois\u2014Black Dead Arm, Institut Fran\u00e7ais de la Vigne et du Vin."},{"key":"ref_16","unstructured":"Denizot, A.M., and Larignon, P. (2008). Description des sympt\u00f4mes des maladies du bois\u2014ESCA, Institut Fran\u00e7ais de la Vigne et du Vin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced methods of plant disease detection. A review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"329","DOI":"10.2135\/cropsci2006.05.0335","article-title":"Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder","volume":"47","author":"Yang","year":"2007","journal-title":"Crop Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.compag.2008.11.007","article-title":"The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars","volume":"66","author":"Naidu","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","unstructured":"Meroni, M., Rossini, M., and Colombo, R. (2010). Characterization of Leaf Physiology Using Reflectance and Fluorescence Hyperspectral Measurements, Research Signpost. Optical Observation of Vegetation Properties and Characteristics."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1746-4811-8-3","article-title":"Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases","volume":"8","author":"Mahlein","year":"2012","journal-title":"Plant Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant Disease Detection by Imaging Sensors\u2014Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping","volume":"100","author":"Mahlein","year":"2015","journal-title":"Plant Dis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"87","DOI":"10.5344\/ajev.2009.60.1.87","article-title":"Nondestructive estimation of anthocyanin content in grapevine leaves","volume":"60","author":"Steele","year":"2009","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1046\/j.1469-8137.1999.00424.x","article-title":"Assessing leaf pigment content and activity with a reflectometer","volume":"143","author":"Gamon","year":"1999","journal-title":"New Phytol."},{"key":"ref_26","first-page":"295","article-title":"Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing","volume":"4","author":"Zhang","year":"2003","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2012.09.019","article-title":"Development of spectral indices for detecting and identifying plant diseases","volume":"128","author":"Mahlein","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/j.1755-0238.2002.tb00209.x","article-title":"Optical remote sensing applications in viticulture\u2014A review","volume":"8","author":"Hall","year":"2002","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_29","unstructured":"Lobitz, B., Johnson, L., Hlavka, C., Armstrong, R., and Bell, C. (1997). Grapevine Remote Sensing Analysis of Phylloxera Early Stress (GRAPES): Remote Sensing Analysis Summary, National Aeronautics and Space Administration. Technical Report."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2016.10.003","article-title":"Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards","volume":"130","author":"MacDonald","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","first-page":"262","article-title":"Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex","volume":"55","author":"Gennaro","year":"2016","journal-title":"Phytopathol. Mediterr."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Laybros, A., and Cointault, F. (2018). Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level. Remote Sens., 10.","DOI":"10.3390\/rs10040618"},{"key":"ref_33","unstructured":"Paindavoine, M., Zunino, P., Brossaud, F., and Cointault, F. (2015). D\u00e9tection de foyers infectieux de Flavescence Dor\u00e9e par imagerie de drone, Les Rencontres du V\u00e9g\u00e9tal. Presented at 8e."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Simon, J., and Cointault, F. (2018). Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: The case of Flavescence dor\u00e9e. Precis. Agric., 1\u201325.","DOI":"10.1007\/s11119-018-9594-1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilv\u00e9, H., F\u00e9ret, J.B., and Dedieu, G. (2017). Detection of Flavescence dor\u00e9e Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_36","unstructured":"Guttler, F., Duthoit, S., Fauvel, M., and Jacquin, A. (2018). Spectral analysis of Vitis vinifera leaves for the detection of the Flavescence dor\u00e9e disease in red and white cultivars. Science, Article in preparation."},{"key":"ref_37","unstructured":"Poilv\u00e9, H. (2010). Towards an Operational GMES Land Monitoring Core Service\u2014BioPar Product User Manual\u2014MERIS FR Biophysical Products, European Research. Technical Report, European Research Project geoland2 (FP7, EC Proposal Reference No.: FP-7-218795)."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"S56","DOI":"10.1016\/j.rse.2008.01.026","article-title":"PROSPECT + SAIL models: A review of use for vegetation characterization","volume":"113","author":"Jacquemoud","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments","volume":"112","author":"Asner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1111\/pce.12332","article-title":"Metabolic and transcript analysis of the flavonoid pathway in diseased and recovered Nebbiolo and Barbera grapevines (Vitis vinifera L.) following infection by Flavescence dor\u00e9e phytoplasma","volume":"37","author":"Margaria","year":"2014","journal-title":"Plant Cell Environ."},{"key":"ref_42","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1007\/s11119-015-9421-x","article-title":"Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale","volume":"17","author":"Yuan","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_46","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11694-009-9070-8","article-title":"Detection of soybean rust using a multispectral image sensor","volume":"3","author":"Cui","year":"2009","journal-title":"Sens. Instrum. Food Qual. Saf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)074<0038:OPANEO>2.0.CO;2","article-title":"Optical properties and nondestructive estimation of anthocyanin content in plant leaves","volume":"74","author":"Gitelson","year":"2001","journal-title":"Photochem. Photobiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"L11402","DOI":"10.1029\/2006GL026457","article-title":"Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves","volume":"33","author":"Gitelson","year":"2006","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MIM.2017.7951684","article-title":"High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging","volume":"20","author":"Patrick","year":"2017","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"685","DOI":"10.21273\/HORTSCI.40.3.685","article-title":"Nondestructive estimation of anthocyanin content in autumn sugar maple leaves","volume":"40","author":"Perkins","year":"2005","journal-title":"HortScience"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"779","DOI":"10.2134\/agronj2007.0254N","article-title":"A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves","volume":"100","author":"Steele","year":"2008","journal-title":"Agron. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1109\/36.934080","article-title":"Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data","volume":"39","author":"Miller","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_56","first-page":"IV","article-title":"The Orfeo Toolbox remote sensing image processing software","volume":"Volume 4","author":"Inglada","year":"2009","journal-title":"Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009)"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_59","first-page":"145","article-title":"La courbe ROC (Receiver operating characteristic): Principes et principales applications en biologie clinique","volume":"63","author":"Delacour","year":"2005","journal-title":"Ann. De Biol. Clin."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1016\/j.rse.2008.01.008","article-title":"Modeling distribution of Amazonian tree species and diversity using remote sensing measurements","volume":"112","author":"Saatchi","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning New York, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_63","first-page":"28","article-title":"The generalization of students\u2019 problem when several different population variances are involved","volume":"34","author":"Welch","year":"1947","journal-title":"Biometrika"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Weiss, M., and Baret, F. (2017). Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sens., 9.","DOI":"10.3390\/rs9020111"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compag.2015.03.011","article-title":"Vineyard detection from unmanned aerial systems images","volume":"114","author":"Comba","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Simon, J.C., and Cointault, F. (2017). Development of Spectral Disease Indices for \u2019Flavescence Dor\u00e9e\u2019 Grapevine Disease Identification. Sensors, 17.","DOI":"10.3390\/s17122772"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/1\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:35:49Z","timestamp":1760196949000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/1\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,23]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11010023"],"URL":"https:\/\/doi.org\/10.3390\/rs11010023","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,23]]}}}