{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:23:10Z","timestamp":1780651390087,"version":"3.54.1"},"reference-count":71,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005908","name":"Bundesministerium f\u00fcr Ern\u00e4hrung und Landwirtschaft","doi-asserted-by":"publisher","award":["FKZ 2815702515"],"award-info":[{"award-number":["FKZ 2815702515"]}],"id":[{"id":"10.13039\/501100005908","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Grapevine yellows (GY) are serious phytoplasma-caused diseases affecting viticultural areas worldwide. At present, two principal agents of GY are known to infest grapevines in Germany: Bois noir (BN) and Palatinate grapevine yellows (PGY). Disease management is mostly based on prophylactic measures as there are no curative in-field treatments available. In this context, sensor-based disease detection could be a useful tool for winegrowers. Therefore, hyperspectral imaging (400\u20132500 nm) was applied to identify phytoplasma-infected greenhouse plants and shoots collected in the field. Disease detection models (Radial-Basis Function Network) have successfully been developed for greenhouse plants of two white grapevine varieties infected with BN and PGY. Differentiation of symptomatic and healthy plants was possible reaching satisfying classification accuracies of up to 96%. However, identification of BN-infected but symptomless vines was difficult and needs further investigation. Regarding shoots collected in the field from different red and white varieties, correct classifications of up to 100% could be reached using a Multi-Layer Perceptron Network for analysis. Thus, hyperspectral imaging seems to be a promising approach for the detection of different GY. Moreover, the 10 most important wavelengths were identified for each disease detection approach, many of which could be found between 400 and 700 nm and in the short-wave infrared region (1585, 2135, and 2300 nm). These wavelengths could be used further to develop multispectral systems.<\/jats:p>","DOI":"10.3390\/rs12244151","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"4151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6757-6026","authenticated-orcid":false,"given":"Nele","family":"Bendel","sequence":"first","affiliation":[{"name":"Institute for Grapevine Breeding Geilweilerhof, Federal Research Centre for Cultivated Plants, Julius K\u00fchn-Institut, 76833 Siebeldingen, Germany"},{"name":"Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Str. 5, 70599 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Backhaus","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Biosystems Engineering, Sandtorstr. 22, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6442-3219","authenticated-orcid":false,"given":"Anna","family":"Kicherer","sequence":"additional","affiliation":[{"name":"Institute for Grapevine Breeding Geilweilerhof, Federal Research Centre for Cultivated Plants, Julius K\u00fchn-Institut, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janine","family":"K\u00f6ckerling","sequence":"additional","affiliation":[{"name":"Institute for Grapevine Breeding Geilweilerhof, Federal Research Centre for Cultivated Plants, Julius K\u00fchn-Institut, 76833 Siebeldingen, Germany"},{"name":"Julius K\u00fchn-Institut, Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Fruit Crops and Viticulture, Geilweilerhof, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2634-0308","authenticated-orcid":false,"given":"Michael","family":"Maixner","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn-Institut, Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Fruit Crops and Viticulture, Geilweilerhof, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Jarausch","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn-Institut, Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Fruit Crops and Viticulture, Geilweilerhof, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Biancu","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn-Institut, Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Fruit Crops and Viticulture, Geilweilerhof, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9097-4911","authenticated-orcid":false,"given":"Hans-Christian","family":"Kl\u00fcck","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Biosystems Engineering, Sandtorstr. 22, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6043-7947","authenticated-orcid":false,"given":"Udo","family":"Seiffert","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation (IFF), Biosystems Engineering, Sandtorstr. 22, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralf T.","family":"Voegele","sequence":"additional","affiliation":[{"name":"Institute of Phytomedicine, University of Hohenheim, Otto-Sander-Str. 5, 70599 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhard","family":"T\u00f6pfer","sequence":"additional","affiliation":[{"name":"Institute for Grapevine Breeding Geilweilerhof, Federal Research Centre for Cultivated Plants, Julius K\u00fchn-Institut, 76833 Siebeldingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rao, G., Bertaccini, A., Fiore, N., and Liefting, L. (2018). Grapevine phytoplasmas. Phytoplasmas: Plant Pathogenic Bacteria-I, Springer.","DOI":"10.1007\/978-981-13-0119-3"},{"key":"ref_2","first-page":"235","article-title":"Diversity of grapevine yellows in Germany","volume":"34","author":"Maixner","year":"1995","journal-title":"Vitis"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1099\/ijs.0.044750-0","article-title":"\u2018Candidatus Phytoplasma solani\u2019, a novel taxon associated with stolbur- and bois noir-related diseases of plants","volume":"63","author":"Quaglino","year":"2013","journal-title":"Int. J. Syst. Evol. Microbiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/j.1439-0434.1998.tb04753.x","article-title":"The role of Hyalesthes obsoletus (Hemiptera: Cixiidae) in the occurrence of bois noir of grapevines in France","volume":"146","author":"Sforza","year":"1998","journal-title":"J. Phytopathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4001","DOI":"10.1128\/AEM.02323-06","article-title":"Multilocus sequence typing confirms the close genetic interrelatedness of three distinct Flavescence dor\u00e9e phytoplasma strain clusters and group 16SrV phytoplasmas infecting grapevine and alder in Europe","volume":"73","author":"Arnaud","year":"2007","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_6","first-page":"83","article-title":"Transmission of grapevine yellows by Oncopsis alni (Schrank)(Auchenorrhyncha: Macropsinae)","volume":"39","author":"Maixner","year":"2000","journal-title":"Vitis"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.1002\/ece3.1158","article-title":"Survival relative to new and ancestral host plants, phytoplasma infection, and genetic constitution in host races of a polyphagous insect disease vector","volume":"4","author":"Maixner","year":"2014","journal-title":"Ecol. Evol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1023\/A:1008602327715","article-title":"Oncopsis alni (Schrank)(Auchenorrhyncha: Cicadellidae) as a vector of the alder yellows phytoplasma of Alnus glutinosa (L.) Gaertn","volume":"105","author":"Maixner","year":"1999","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bertaccini, A., Weintraub, P., Rao, G., and Mori, N. (2019). Transmission of phytoplasmas by agronomic practices. Phytoplasmas: Plant Pathogenic Bacteria-II, Springer.","DOI":"10.1007\/978-981-13-2832-9"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.4236\/ajps.2014.512191","article-title":"Phytoplasmas and phytoplasma diseases: A severe threat to agriculture","volume":"5","author":"Bertaccini","year":"2014","journal-title":"Am. J. Plant Sci."},{"key":"ref_11","first-page":"303","article-title":"Grapevine yellows in Italy: Past, present and future","volume":"92","author":"Belli","year":"2010","journal-title":"J. Plant Pathol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.3389\/fpls.2016.01762","article-title":"Contrasting susceptibilities to Flavescence dor\u00e9e in Vitis vinifera, rootstocks and wild Vitis species","volume":"7","author":"Eveillard","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_13","unstructured":"Maixner, M. (2006, January 3\u20137). Grapevine yellows\u2014Current developments and unsolved questions. Proceedings of the 15th Meeting of ICVG, Stellenbosch, South Africa."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bertaccini, A., Weintraub, P., Rao, G., and Mori, N. (2019). Integrated management of phytoplasma diseases. Phytoplasmas: Plant Pathogenic Bacteria-II, Springer.","DOI":"10.1007\/978-981-13-2832-9"},{"key":"ref_15","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":"2016","journal-title":"Plant Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s42483-020-00049-8","article-title":"From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy","volume":"2","author":"Bock","year":"2020","journal-title":"Phytopathol. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.3389\/fpls.2016.01377","article-title":"Non-invasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet","volume":"7","author":"Arens","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.biosystemseng.2013.05.010","article-title":"Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision","volume":"117","author":"Polder","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Behmann, J., Bohnenkamp, D., Paulus, S., and Mahlein, A.-K. (2018). Spatial referencing of hyperspectral images for tracing of plant disease symptoms. J. Imaging, 4.","DOI":"10.3390\/jimaging4120143"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431160802541556","article-title":"Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology","volume":"30","author":"Delalieux","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-015-0073-7","article-title":"Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant-pathogen interactions","volume":"11","author":"Kuska","year":"2015","journal-title":"Plant Methods"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5958\/2249-4677.2019.00040.9","article-title":"Near-infrared spectroscopy analysis\u2014A useful tool to detect apple proliferation diseased trees?","volume":"9","author":"Barthel","year":"2019","journal-title":"Phytopathog. Mollicutes"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilv\u00e9, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2018). On the potentiality of UAV multispectral imagery to detect Flavescence dor\u00e9e and grapevine trunk diseases. Remote Sens., 11.","DOI":"10.3390\/rs11010023"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Simon, J.C., and Cointault, F. (2017). Development of spectral disease indices for \u2018Flavescence dor\u00e9e\u2019 grapevine disease identification. Sensors, 17.","DOI":"10.3390\/s17122772"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Laybros, A., Billiot, B., 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_27","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1007\/s11119-018-9594-1","article-title":"Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: The case of \u201cFlavescence dor\u00e9e\u201d","volume":"20","author":"Simon","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/BF01874780","article-title":"Detection of the German grapevine yellows (Vergilbungskrankheit) MLO in grapevine, alternative hosts and a vector by a specific PCR procedure","volume":"101","author":"Maixner","year":"1995","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1094\/Phyto-85-771","article-title":"Detection of the apple proliferation and pear decline phytoplasmas by PCR amplification of ribosomal and nonribosomal DNA","volume":"85","author":"Lorenz","year":"1995","journal-title":"Phytopathology"},{"key":"ref_30","first-page":"369","article-title":"Phylogenetic classification of plant pathogenic mycoplasma-like organisms or phytoplasmas","volume":"Volume 1","author":"Razin","year":"1995","journal-title":"Molecular and Diagnostic Procedures in Mycoplasmology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1094\/PD-80-0669","article-title":"Detection of an elm yellows-related phytoplasma in eucalyptus trees affected by little-leaf disease in Italy","volume":"80","author":"Marcone","year":"1996","journal-title":"Plant Dis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bendel, N., Kicherer, A., Backhaus, A., K\u00f6ckerling, J., Maixner, M., Bleser, E., Kl\u00fcck, H.-C., Seiffert, U., Voegele, R.T., and T\u00f6pfer, R. (2020). Detection of grapevine leafroll-associated virus 1 and 3 in white and red grapevine cultivars using hyperspectral imaging. Remote Sens., 12.","DOI":"10.3390\/rs12101693"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s13007-020-00685-3","article-title":"Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards","volume":"16","author":"Bendel","year":"2020","journal-title":"Plant Methods"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math Control Sign. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2018.02.003","article-title":"Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform","volume":"138","author":"Asaari","year":"2018","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_36","unstructured":"Fortuna, L., Graziani, S., Rizzo, A., and Xibilia, M.G. (2007). Soft Sensors for Monitoring and Control of Industrial Processes, Springer Science & Business Media."},{"key":"ref_37","unstructured":"Krzanowski, W. (1988). Principles of Multivariate Analysis: A User\u2019s Perspective, Clarendon Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/S0893-6080(05)80056-5","article-title":"A scaled conjugate gradient algorithm for fast supervised learning","volume":"6","year":"1993","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1162\/neco.1989.1.2.281","article-title":"Fast learning in networks of locally-tuned processing units","volume":"1","author":"Moody","year":"1989","journal-title":"Neural Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Backhaus, A., Bollenbeck, F., and Seiffert, U. (2011, January 6\u20139). Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (Whispers), Lisbon, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080898"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11075-018-0591-2","article-title":"Scaled nonlinear conjugate gradient methods for nonlinear least squares problems","volume":"82","author":"Dehghani","year":"2019","journal-title":"Numer. Algorithms"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Becker, F., Backhaus, A., Johrden, F., and Flitter, M. (2020). Optimal multispectral sensor configurations through machine learning for cognitive agriculture. Automatisierungstechnik Spec. Issue Cognetive Agric., Accepted for Publication.","DOI":"10.1515\/auto-2020-0069"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/72.238311","article-title":"\u2018Neural-gas\u2019 network for vector quantization and its application to time-series prediction","volume":"4","author":"Martinetz","year":"1993","journal-title":"IEEE Transact. Neural Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.postharvbio.2018.01.018","article-title":"Detection of fungal infections in strawberry fruit by VNIR\/SWIR hyperspectral imaging","volume":"139","author":"Siedliska","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wiegmann, M., Backhaus, A., Seiffert, U., Thomas, W.T., Flavell, A.J., Pillen, K., and Maurer, A. (2019). Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224491"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Ehsani, R., and de Castro, A. (2016). Detection and differentiation between laurel wilt disease, phytophtora disease, and salinity damage using hyperspectral sensing technique. Agriculture, 6.","DOI":"10.3390\/agriculture6040056"},{"key":"ref_48","first-page":"365","article-title":"Uneven distribution of stolbur phytoplasma in Italian grapevines as revealed by nested-PCR","volume":"60","author":"Terlizzi","year":"2007","journal-title":"Bull. Insect."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.compag.2017.08.001","article-title":"Identification of asymptomatic plants infected with Citrus tristeza virus from a time series of leaf spectral characteristics","volume":"141","author":"Afonso","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","first-page":"311","article-title":"Hot water treatment and field coverage of mother plant vineyards to prevent propagation material from phytoplasma infections","volume":"60","author":"Mannini","year":"2007","journal-title":"Bull. Insect."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1038\/s41598-019-40066-y","article-title":"Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN)","volume":"9","author":"Wang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2016.01.012","article-title":"Strawberry foliar anthracnose assessment by hyperspectral imaging","volume":"122","author":"Yeh","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s13007-017-0198-y","article-title":"Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images","volume":"13","author":"Knauer","year":"2017","journal-title":"Plant Methods"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1094\/MPMI-08-12-0207-R","article-title":"Phytoplasma-triggered Ca2+ influx is involved in sieve-tube blockage","volume":"26","author":"Musetti","year":"2013","journal-title":"Mol. Plant Microbe Interact."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hren, M., Nikolic, P., Rotter, A., Blejec, A., Terrier, N., Ravnikar, M., Dermastia, M., and Gruden, K. (2009). \u2018Bois noir\u2019 phytoplasma induces significant reprogramming of the leaf transcriptome in the field grown grapevine. BMC Genom., 10.","DOI":"10.1186\/1471-2164-10-460"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1006\/pmpp.2003.0449","article-title":"Phytoplasma [Stolbur-subgroup (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","volume":"61","author":"Bertamini","year":"2002","journal-title":"Physiol. Mol. Plant Path."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral remote sensing of plant pigments","volume":"58","author":"Blackburn","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S1360-1385(98)01213-8","article-title":"Visible and near-infrared reflectance techniques for diagnosing plant physiological status","volume":"3","author":"Filella","year":"1998","journal-title":"Trends Plant Sci."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1111\/j.1365-313X.2006.02997.x","article-title":"White grapes arose through the mutation of two similar and adjacent regulatory genes","volume":"49","author":"Walker","year":"2007","journal-title":"Plant J."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.tplants.2005.09.008","article-title":"Phytoplasmas and their interactions with hosts","volume":"10","author":"Christensen","year":"2005","journal-title":"Trends Plant Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Negro, C., Sabella, E., Nicol\u00ec, F., Pierro, R., Materazzi, A., Panattoni, A., Aprile, A., Nutricati, E., Vergine, M., and Miceli, A. (2020). Biochemical changes in leaves of Vitis vinifera cv. Sangiovese infected by Bois noir phytoplasma. Pathogens, 9.","DOI":"10.3390\/pathogens9040269"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0034-4257(99)00082-6","article-title":"Plant litter and soil reflectance","volume":"71","author":"Nagler","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.07.007","article-title":"Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves","volume":"168","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4939-8837-2_1","article-title":"Phytoplasmas: An introduction","volume":"Volume 1875","author":"Musetti","year":"2019","journal-title":"Phytoplasmas. Methods in Molecular Biology"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2019.04.008","article-title":"Visible-near infrared spectroradiometry-based detection of grapevine leafroll-associated virus 3 in a red-fruited wine grape cultivar","volume":"162","author":"Sinha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/978-1-4939-8837-2_17","article-title":"Protocol for the definition of a multi-spectral sensor for specific foliar disease detection: Case of \u201cFlavescence dor\u00e9e\u201d","volume":"Volume 1875","author":"Musetti","year":"2019","journal-title":"Phytoplasmas. Methods in Molecular Biology"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:05Z","timestamp":1760179625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,18]]},"references-count":71,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244151"],"URL":"https:\/\/doi.org\/10.3390\/rs12244151","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,18]]}}}