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A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologies

2025Comunicação em conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosCiências Agrárias::Biotecnologia Agrária e Alimentar
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg12:Produção e Consumo Sustentáveis
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorMorais, Maurício Herche Fófano de
dc.contributor.authorMendes, João
dc.contributor.authorSantos, Murillo Ferreira dos
dc.contributor.authorFernandes, Fernanda Mara
dc.contributor.authorLima, José
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2026-03-16T15:25:55Z
dc.date.available2026-03-16T15:25:55Z
dc.date.issued2025
dc.date.updated2026-03-11T19:41:09Z
dc.description.abstractThis work presents an approach for detecting olive knot disease in olive trees, utilizing Computer Vision (CV), Unmanned Aerial Vehicle (UAV) based imagery, and Machine Learning (ML) within the context of Precision Agriculture (PA). The study focuses on applying the You Only Look Once (YOLO) deep learning architecture to develop a model capable of identifying trees affected by the disease with accuracy and speed. By integrating UAV technology with object detection algorithms, this approach enables real-time monitoring of olive plantations, supporting early detection and targeted interventions. This study emphasizes the potential of combining drone imaging and ML to drive sustainable and practical solutions in PA. Results show that this method can potentially improve crop management by reducing human labor and contributing to the enhancement of disease control strategies.eng
dc.description.sponsorshipThe authors are grateful to CeDRI (UID/05757), SusTEC (LA/P/0007/2021), and Olive Grove to Fork 4.0 project, funding by NORTE2030-FEDER-01182700, the National Council for Scientific and Technological Development – CNPq, related to project 442696/2023-0. They also thank CEFET-MG, IPB, and FAMINAS.
dc.description.versionN/A
dc.identifier.citationMorais, Maurício Herche Fófano de; Mendes, João; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Pereira, Ana I. (2025). A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologies. In 26th International Carpathian Control Conference, ICCC 2025. Stary Smokovec:IEEE. p. 1-6. ISBN 979-833150127-3
dc.identifier.doi10.1109/iccc65605.2025.11022835en_US
dc.identifier.eid2-s2.0-105009015163en_US
dc.identifier.isbn979-833150127-3
dc.identifier.urihttp://hdl.handle.net/10198/36087
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectUnmanned aerial vehicle
dc.subjectDisease detection
dc.subjectOlive knot
dc.subjectYOLOen_US
dc.subjectComputer vision
dc.subjectDeep learning
dc.titleA YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologieseng
dc.typeconference paperen_US
dspace.entity.typePublication
oaire.awardNumberUIDP/05757/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIhttp://hdl.handle.net/10198/35997
oaire.citation.conferencePlaceEslováquiaen_US
oaire.citation.endPage6
oaire.citation.startPage1
oaire.citation.title26th International Carpathian Control Conference, ICCC 2025
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLima
person.familyNamePereira
person.givenNameJosé
person.givenNameAna I.
person.identifierR-000-8GD
person.identifier.ciencia-id6016-C902-86A9
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0001-7902-1207
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridL-3370-2014
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id55851941311
person.identifier.scopus-author-id15071961600
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
rcaap.cv.cienciaid1517-A459-54E4 | Maurício Herche Fófano de Morais
rcaap.rightsclosedAccessen_US
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscoveryd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isProjectOfPublicationd0a17270-80a8-4985-9644-a04c2a9f2dff
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