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dc.creatorPérez Roncal, Claudiaes_ES
dc.creatorArazuri Garín, Silviaes_ES
dc.creatorLópez Molina, Carloses_ES
dc.creatorJarén Ceballos, Carmenes_ES
dc.creatorSantesteban García, Gonzagaes_ES
dc.creatorLópez Maestresalas, Ainaraes_ES
dc.date.accessioned2022-05-25T12:19:22Z
dc.date.available2022-05-25T12:19:22Z
dc.date.issued2022
dc.identifier.citationPérez-Roncal, C.; Arazuri, S.; Lopez-Molina, C.; Jarén, C.; Santesteban, L. G.; López-Maestresalas, A.. (2022). Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves. Computers and Electronics in Agriculture. 196,106863 1-12 .en
dc.identifier.issn0168-1699
dc.identifier.urihttps://hdl.handle.net/2454/43022
dc.description.abstractPrecise and reliable identification of specific plant diseases is a challenge within precision agriculture nowadays. This is the case of esca, a complex grapevine trunk disease, that represents a major threat to modern viticulture as it is responsible for large economic losses annually. The lack of effective control strategies and the complexity of esca disease expression make essential the identification of affected plants, before symptoms become evident, for a better management of the vineyard. This study evaluated the suitability of a near-infrared hyperspectral imaging (HSI) system to detect esca disease in asymptomatic grapevine leaves of Tempranillo red-berried cultivar. For this, 72 leaves from an experimental vineyard, naturally infected with esca, were collected and scanned with a lab-scale HSI system in the 900-1700 nm spectral range. Then, effective image processing and multivariate analysis techniques were merged to develop pixel-based classification models for the distinction of healthy, asymptomatic and symptomatic leaves. Automatic and interval partial least squares variable selection methods were tested to identify the most relevant wavelengths for the detection of esca-affected vines using partial least squares discriminant analysis and different pre-processing techniques. Three-class and two-class classifiers were carried out to differentiate healthy, asymptomatic and symptomatic leaf pixels, and healthy from asymptomatic pixels, respectively. Both variable selection methods performed similarly, achieving good classification rates in the range of 82.77-97.17% in validation datasets for either three-class or two-class classifiers. The latter results demonstrated the capability of hyperspectral imaging to distinguish two groups of seemingly identical leaves (healthy and asymptomatic). These findings would ease the annual monitoring of disease incidence in the vineyard and, therefore, better crop management and decision making.en
dc.description.sponsorshipThis research was supported by Public University of Navarre postgraduate scholarships (FPI-UPNA-2017), by the Spanish Ministry of Economy and Competitiveness (AGL2017-83738-C3-1R, AEI/EU-FEDER), and by the Spanish Ministry of Science, Innovation and Universities (PID2019-108392GB-I00, AEI/10.13039/ 501100011033).en
dc.format.extent12 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofComputers And Electronics In Agriculture, 2022, 196 (106863),1-12en
dc.rights© 2022 The Authors. This is an open access article under the CC BY-NC-ND licenseen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHyperspectral imagingen
dc.subjectDisease detectionen
dc.subjectGrapevine trunk diseaseen
dc.subjectPrecision viticultureen
dc.subjectPixel-based classificationen
dc.titleExploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leavesen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.date.updated2022-05-25T12:17:50Z
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODes_ES
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.contributor.departmentAgronomía, Biotecnología y Alimentaciónes_ES
dc.contributor.departmentAgronomia, Bioteknologia eta Elikaduraeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1016/j.compag.2022.106863
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83738-C3-1-R/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.1016/j.compag.2022.106863
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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© 2022 The Authors. This is an open access article under the CC BY-NC-ND license
Except where otherwise noted, this item's license is described as © 2022 The Authors. This is an open access article under the CC BY-NC-ND license

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