On-site identification of esca-affected vines using hyperspectral imaging

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dc.contributor.authorLeón Ecay, Sara
dc.contributor.authorRuiz de Gauna González, Jon
dc.contributor.authorLópez Maestresalas, Ainara
dc.contributor.authorJarén Ceballos, Carmen
dc.contributor.authorArazuri Garín, Silvia
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.departmentAgronomía, Biotecnología y Alimentaciónes_ES
dc.contributor.departmentAgronomia, Bioteknologia eta Elikaduraeu
dc.contributor.departmentInstitute on Innovation and Sustainable Development in Food Chain - ISFOODen
dc.date.accessioned2025-05-07T09:06:42Z
dc.date.available2025-05-07T09:06:42Z
dc.date.issued2025
dc.date.updated2025-05-07T09:02:10Z
dc.description.abstractEsca represents one of the greatest threats to modern viticulture as it causes large annual economic losses. At present, there is a lack of effective strategies for disease control, so a technique capable of detecting affected vines would allow annual monitoring of disease incidence in the vineyard leading to a better crop management and decision making. This study evaluates close-range hyperspectral imaging for the detection of esca naturally infected vines. Images of 11 vines of the Tempranillo variety grown on plots in Bodegas Otazu, in Etxauri (Navarre, Spain) were acquired. A Specim IQ snapshot hyperspectral camera was used to record the images on August, 21 2023 on the field under natural light conditions. The camera has a spectral resolution of 7 nm (204 wavelengths) and a spatial resolution of 512 x 512 in the 400 ¿ 1000 nm spectral range (Vis-NIR). An individual image was acquired for each vine, of which 9 were symptomatic and 2 asymptomatic. Three classes were analysed: asymptomatic leaves of asymptomatic vines (Class 1), asymptomatic leaves of symptomatic vines (Class 2) and asymptomatic areas of symptomatic leaves of symptomatic vines (Class 3). A total of 300 pixels were randomly selected, 100 per class, for further analysis. Partial Least Square Discriminant Analysis (PLSDA) was used to classify the pixels into the three categories. An accuracy of 86% was achieved in the cross-validation dataset. Models were externally validated using an image of an asymptomatic vine and an image of a symptomatic vine. The visualisation of the images showed that the majority of the pixels of the asymptomatic vine image were classified as class 1, while most of the pixels of the symptomatic vine image were classified as either class 2 or class 3. Hence, this study demonstrated the potential of close-range HSI for the on-site detection of esca.en
dc.description.sponsorshipThis research was supported by the Spanish Ministry of Science and Innovation and from the European Union Next-Generation EU/PRTR (TED2021-130364B-I00).
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLeón-Ecay, S., Ruiz de Gauna, J., López-Maestresalas, A., Jarén, C., Arazuri, S. (2025). On-site identification of esca-affected vines using hyperspectral imaging. In Katsoulas N. (Ed.), Agricultural Engineerring challenges in existing and new agroecosystems: 1-4 July 2024, Agricultural University of Athens, Greece. (pp. 28-33). Hellenic Society of Agricultural Engineers.
dc.identifier.isbn978-618-82194-1-0
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/54082
dc.language.isoeng
dc.publisherHellenic Society of Agricultural Engineers
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130364B-I00/
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectTrunk diseaseen
dc.subjectAccuracyen
dc.subjectChemometricsen
dc.subjectImageen
dc.subjectVis-NIRen
dc.titleOn-site identification of esca-affected vines using hyperspectral imagingen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication8f62db26-219d-4520-b17f-d0d47ca60c29
relation.isAuthorOfPublicationbc607da1-a1ab-4216-be92-08409b033643
relation.isAuthorOfPublication5e3fa1e8-fa68-40e6-aa60-e3be4219af67
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relation.isAuthorOfPublication.latestForDiscovery5e3fa1e8-fa68-40e6-aa60-e3be4219af67

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