Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves

Precise 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...

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Autores: Pérez Roncal, Claudia, Arazuri Garín, Silvia, López Molina, Carlos, Jarén Ceballos, Carmen, Santesteban García, Gonzaga, López Maestresalas, Ainara
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/43022
Acceso en línea:https://hdl.handle.net/2454/43022
Access Level:acceso abierto
Palabra clave:Hyperspectral imaging
Disease detection
Grapevine trunk disease
Precision viticulture
Pixel-based classification
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spelling Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leavesPérez Roncal, ClaudiaArazuri Garín, SilviaLópez Molina, CarlosJarén Ceballos, CarmenSantesteban García, GonzagaLópez Maestresalas, AinaraHyperspectral imagingDisease detectionGrapevine trunk diseasePrecision viticulturePixel-based classificationPrecise 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.This 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).ElsevierIngeniaritzaEstatistika, Informatika eta MatematikaAgronomia, Bioteknologia eta ElikaduraInstitute on Innovation and Sustainable Development in Food Chain - ISFOODIngenieríaEstadística, Informática y MatemáticasAgronomía, Biotecnología y AlimentaciónUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/43022reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83738-C3-1-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00© 2022 The Authors. This is an open access article under the CC BY-NC-ND licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/430222026-06-17T12:41:47Z
dc.title.none.fl_str_mv Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
title Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
spellingShingle Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
Pérez Roncal, Claudia
Hyperspectral imaging
Disease detection
Grapevine trunk disease
Precision viticulture
Pixel-based classification
title_short Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
title_full Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
title_fullStr Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
title_full_unstemmed Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
title_sort Exploring the potential of hyperspectral imaging to detect Esca disease complex in asymptomatic grapevine leaves
dc.creator.none.fl_str_mv Pérez Roncal, Claudia
Arazuri Garín, Silvia
López Molina, Carlos
Jarén Ceballos, Carmen
Santesteban García, Gonzaga
López Maestresalas, Ainara
author Pérez Roncal, Claudia
author_facet Pérez Roncal, Claudia
Arazuri Garín, Silvia
López Molina, Carlos
Jarén Ceballos, Carmen
Santesteban García, Gonzaga
López Maestresalas, Ainara
author_role author
author2 Arazuri Garín, Silvia
López Molina, Carlos
Jarén Ceballos, Carmen
Santesteban García, Gonzaga
López Maestresalas, Ainara
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniaritza
Estatistika, Informatika eta Matematika
Agronomia, Bioteknologia eta Elikadura
Institute on Innovation and Sustainable Development in Food Chain - ISFOOD
Ingeniería
Estadística, Informática y Matemáticas
Agronomía, Biotecnología y Alimentación
Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.subject.none.fl_str_mv Hyperspectral imaging
Disease detection
Grapevine trunk disease
Precision viticulture
Pixel-based classification
topic Hyperspectral imaging
Disease detection
Grapevine trunk disease
Precision viticulture
Pixel-based classification
description Precise 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.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/43022
url https://hdl.handle.net/2454/43022
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-83738-C3-1-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00
dc.rights.none.fl_str_mv © 2022 The Authors. This is an open access article under the CC BY-NC-ND license
https://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2022 The Authors. This is an open access article under the CC BY-NC-ND license
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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