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Identification of aphids using machine learning classifiers on UAV-based multispectral data

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dc.contributor.authorGuimarães, Nathalie
dc.contributor.authorPádua, Luís
dc.contributor.authorSousa, Joaquim J.
dc.contributor.authorBento, Albino
dc.contributor.authorCouto, Pedro
dc.date.accessioned2024-01-30T16:44:58Z
dc.date.available2024-01-30T16:44:58Z
dc.date.issued2023
dc.description.abstractAlmond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.pt_PT
dc.description.sponsorshipFinancial support was provided by national funds through FCT – Portuguese Foundation for Science and Technology (UI/BD/150727/2020), under the Doctoral Programme “Agricultural Production Chains – from fork to farm” (PD/00122/2012) and from the European Social Funds and the Regional Operational Programme Norte 2020. This study was also supported by CITAB research unit (UIDB/04033/2020), Inov4Agro (LA/P/0126/2020) and by CIMO (UIDB/00690/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGuimarães, Nathalie; Pádua, Luís; Sousa, Joaquim J.; Bento, Albino; Couto, Pedro (2023). Identification of aphids using machine learning classifiers on UAV-based multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). p. 3462-3465. ISBN 979-8-3503-2010-7pt_PT
dc.identifier.doi10.1109/IGARSS52108.2023.10281655pt_PT
dc.identifier.isbn979-8-3503-2010-7
dc.identifier.urihttp://hdl.handle.net/10198/29401
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationAerial high-resolution imagery to assess almond orchard conditions
dc.relationIon beam modification of advanced wide bandgap semiconductor hetero- and nanostructures
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationInstitute for innovation, capacity building and sustainability of agri-food production
dc.relationMountain Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAlmond orchardpt_PT
dc.subjectVegetation indicespt_PT
dc.subjectMachine learningpt_PT
dc.subjectSupport vector machinept_PT
dc.titleIdentification of aphids using machine learning classifiers on UAV-based multispectral datapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleAerial high-resolution imagery to assess almond orchard conditions
oaire.awardTitleIon beam modification of advanced wide bandgap semiconductor hetero- and nanostructures
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleInstitute for innovation, capacity building and sustainability of agri-food production
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_NORTE/UI%2FBD%2F150727%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Investigador FCT/IF%2F00122%2F2012%2FCP0171%2FCT0001/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0126%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.citation.endPage3465pt_PT
oaire.citation.startPage3462pt_PT
oaire.citation.title2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)pt_PT
oaire.fundingStreamPOR_NORTE
oaire.fundingStreamInvestigador FCT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBento
person.givenNameAlbino
person.identifier.ciencia-idD516-325A-9AD7
person.identifier.orcid0000-0001-5215-785X
person.identifier.ridN-9706-2016
person.identifier.scopus-author-id35247694000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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