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Automated preprocessing of olive leaf images for cultivar classification using YOLO11

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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia Mecânica
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg02:Erradicar a Fome
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorMendes, João
dc.contributor.authorLima, José
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-12-04T15:38:23Z
dc.date.available2025-12-04T15:38:23Z
dc.date.issued2026
dc.description.abstractOlive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.eng
dc.description.sponsorshipThis work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).
dc.identifier.citationMendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I. (2026). Automated Preprocessing of Olive Leaf Images for Cultivar Classification Using YOLO11. In 5th International Conference OL2A. Cham: Springer Nature. p. 286–297. ISBN 9783032001368
dc.identifier.doi10.1007/978-3-032-00137-5_20
dc.identifier.isbn9783032001368
dc.identifier.isbn9783032001375
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10198/35171
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofOptimization, Learning Algorithms and Applications
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectOlive Cultivation
dc.subjectLeaf Detection
dc.subjectImage Segmentation
dc.subjectYOLO11
dc.subjectSmart Agriculture
dc.titleAutomated preprocessing of olive leaf images for cultivar classification using YOLO11eng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberUIDP/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions - LA/P/0007/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.endPage297
oaire.citation.startPage286
oaire.citation.titleOptimization, Learning Algorithms and Applications: 5th International Conference - Part 1
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMendes
person.familyNameLima
person.familyNameRodrigues
person.familyNamePereira
person.givenNameJoão
person.givenNameJosé
person.givenNameNuno
person.givenNameAna I.
person.identifier2726655
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person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0001-7902-1207
person.identifier.orcid0000-0002-9305-0976
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridL-3370-2014
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person.identifier.scopus-author-id57225794972
person.identifier.scopus-author-id55851941311
person.identifier.scopus-author-id55258560600
person.identifier.scopus-author-id15071961600
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
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