Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits

Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can al...

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Detalhes bibliográficos
Autores: Poblete, Tomás, Navas Cortés, Juan Antonio, Camino, Carlos, Calderón Madrid, Rocío, Hornero, Alberto, González-Dugo, Victoria, Landa, Blanca B., Zarco-Tejada, Pablo J.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2021
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/267858
Acesso em linha:http://hdl.handle.net/10261/267858
Access Level:Acceso aberto
Palavra-chave:Hyperspectral
Thermal
Machine learning
Plant traits
Verticillium dahliae
Xylella fastidiosa
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spelling Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traitsPoblete, TomásNavas Cortés, Juan AntonioCamino, CarlosCalderón Madrid, RocíoHornero, AlbertoGonzález-Dugo, VictoriaLanda, Blanca B.Zarco-Tejada, Pablo J.HyperspectralThermalMachine learningPlant traitsVerticillium dahliaeXylella fastidiosaGlobally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can also be confounded with water stress. Conventional in situ monitoring techniques are time consuming and expensive, necessitating the development of large-scale detection methods. Airborne hyperspectral and thermal imagery have been successfully used to detect both Xf and Vd infection symptoms independently, i.e., when only one of the two diseases is present. Nevertheless, the discrimination of Vd from Xf infections in contexts where both pathogens are present has not been addressed to date. This study proposes a three-stage machine learning algorithm to distinguish Vd infections from Xf infections, using a series of datasets from 27 olive orchards affected by Xf and Vd outbreaks in Italy and Spain between 2011 and 2017. Plant traits were derived from airborne hyperspectral and thermal imagery, including physiological indices from radiative transfer model inversion, Solar-induced Fluorescence emission (SIF@760), the Crop Water Stress Index (CWSI), and a selection of narrow–band hyperspectral indices. Several distinct spectral traits successfully discriminated Xf from Vd infections. The three-stage method generated a false-positive rate of 9%, an overall accuracy (OA) of 98%, and a kappa coefficient (κ) of 0.7 when identifying Vd infections using a mixed Vd + Xf dataset. When identifying Xf infections, the false-positive rate was 4%, the OA was 92%, and κ was 0.8. These results indicate that hyperspectral and thermal traits can be used to discriminate Xf from Vd infection caused by the two xylem–limited pathogens that trigger similar visual symptoms.The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 and P18-RT-4184 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from “Programa Estatal de I + D + I Orientada a los Retos de la Sociedad” of Spain and FEDER, Intramural Project 201840E111 from CSIC.ElsevierEuropean CommissionMinisterio de Educación y Ciencia (España)Junta de AndalucíaConsejo Superior de Investigaciones Científicas (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220212022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/267858reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/635646info:eu-repo/grantAgreement/EC/H2020/727987http://dx.doi.org/10.1016/j.isprsjprs.2021.07.014Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2678582026-05-22T06:33:51Z
dc.title.none.fl_str_mv Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
title Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
spellingShingle Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
Poblete, Tomás
Hyperspectral
Thermal
Machine learning
Plant traits
Verticillium dahliae
Xylella fastidiosa
title_short Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
title_full Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
title_fullStr Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
title_full_unstemmed Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
title_sort Discriminating Xylella fastidiosa from Verticillium dahliae infections in olive trees using thermal- and hyperspectral-based plant traits
dc.creator.none.fl_str_mv Poblete, Tomás
Navas Cortés, Juan Antonio
Camino, Carlos
Calderón Madrid, Rocío
Hornero, Alberto
González-Dugo, Victoria
Landa, Blanca B.
Zarco-Tejada, Pablo J.
author Poblete, Tomás
author_facet Poblete, Tomás
Navas Cortés, Juan Antonio
Camino, Carlos
Calderón Madrid, Rocío
Hornero, Alberto
González-Dugo, Victoria
Landa, Blanca B.
Zarco-Tejada, Pablo J.
author_role author
author2 Navas Cortés, Juan Antonio
Camino, Carlos
Calderón Madrid, Rocío
Hornero, Alberto
González-Dugo, Victoria
Landa, Blanca B.
Zarco-Tejada, Pablo J.
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Educación y Ciencia (España)
Junta de Andalucía
Consejo Superior de Investigaciones Científicas (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Hyperspectral
Thermal
Machine learning
Plant traits
Verticillium dahliae
Xylella fastidiosa
topic Hyperspectral
Thermal
Machine learning
Plant traits
Verticillium dahliae
Xylella fastidiosa
description Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can also be confounded with water stress. Conventional in situ monitoring techniques are time consuming and expensive, necessitating the development of large-scale detection methods. Airborne hyperspectral and thermal imagery have been successfully used to detect both Xf and Vd infection symptoms independently, i.e., when only one of the two diseases is present. Nevertheless, the discrimination of Vd from Xf infections in contexts where both pathogens are present has not been addressed to date. This study proposes a three-stage machine learning algorithm to distinguish Vd infections from Xf infections, using a series of datasets from 27 olive orchards affected by Xf and Vd outbreaks in Italy and Spain between 2011 and 2017. Plant traits were derived from airborne hyperspectral and thermal imagery, including physiological indices from radiative transfer model inversion, Solar-induced Fluorescence emission (SIF@760), the Crop Water Stress Index (CWSI), and a selection of narrow–band hyperspectral indices. Several distinct spectral traits successfully discriminated Xf from Vd infections. The three-stage method generated a false-positive rate of 9%, an overall accuracy (OA) of 98%, and a kappa coefficient (κ) of 0.7 when identifying Vd infections using a mixed Vd + Xf dataset. When identifying Xf infections, the false-positive rate was 4%, the OA was 92%, and κ was 0.8. These results indicate that hyperspectral and thermal traits can be used to discriminate Xf from Vd infection caused by the two xylem–limited pathogens that trigger similar visual symptoms.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/267858
url http://hdl.handle.net/10261/267858
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/635646
info:eu-repo/grantAgreement/EC/H2020/727987
http://dx.doi.org/10.1016/j.isprsjprs.2021.07.014
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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