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An Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills

2024Conference paper Restricted access
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dc.contributor.authorMendes, João
dc.contributor.authorLima, José
dc.contributor.authorCosta, Lino
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorLeitão, Paulo
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-10-07T13:41:56Z
dc.date.available2024-10-07T13:41:56Z
dc.date.issued2024
dc.description.abstractIdentifying the maturation stage is an added value for olive oil producers and consumers, whether this is done to predict the best harvest time, give us more information about the olive oil, or even adapt techniques and extraction parameters in the olive oil mill. In this way, the proposed work presents a new method to identify and count the number of olives that enter the mill as well as their stage of maturation. It is based on artificial intelligence (AI) and deep learning algorithms, using the two most recent versions of YOLO, YOLOv7 and YOLOv8. The obtained results demonstrate the possibility of using this type of application in a real environment, managing to obtain a mAP of approximately 79% with YOLOv8 in the five maturation stages, with a processing rate of approximately 16 FPS increasing this with YOLOv7 to 36.5 FPS reaching a 66% mAP.pt_PT
dc.description.sponsorshipThis work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF). The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendes, João; Lima, José; Costa, Lino A.; Rodrigues, Nuno; Leitão, Paulo; Pereira, Ana I. (2024). An Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 63–77. ISBN 978-3-031-53035-7.pt_PT
dc.identifier.doi10.1007/978-3-031-53036-4_5pt_PT
dc.identifier.isbn978-3-031-53035-7
dc.identifier.isbn978-3-031-53036-4
dc.identifier.urihttp://hdl.handle.net/10198/30326
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationALGORITMI Research Center
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectOlive ripening stagespt_PT
dc.subjectYou Only Look Once (YOLO)pt_PT
dc.subjectIntelligentpt_PT
dc.subjectSystempt_PT
dc.subjectComputer Visionpt_PT
dc.subjectPrecision Agriculturept_PT
dc.titleAn Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Millspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleALGORITMI Research Center
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
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/UIDB%2F00319%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage77pt_PT
oaire.citation.startPage63pt_PT
oaire.citation.title3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMendes
person.familyNameLima
person.familyNameRodrigues
person.familyNameLeitão
person.familyNamePereira
person.givenNameJoão
person.givenNameJosé
person.givenNameNuno
person.givenNamePaulo
person.givenNameAna I.
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person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0001-7902-1207
person.identifier.orcid0000-0002-9305-0976
person.identifier.orcid0000-0002-2151-7944
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridL-3370-2014
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person.identifier.scopus-author-id57225794972
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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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.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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