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Deep learning networks for olive cultivar identification: a comprehensive analysis of convolutional neural networks

2024Journal article Open access
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dc.contributor.authorMendes, João
dc.contributor.authorLima, José
dc.contributor.authorCosta, Lino
dc.contributor.authorRodrigues, Nuno
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2024-06-13T09:11:22Z
dc.date.available2024-06-13T09:11:22Z
dc.date.issued2024
dc.description.abstractDeep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques.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) and was supported by international funds STEP, HORIZON-WIDERA-2021-ACCESS- 03-01, n. 101078933. 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 (DOI: 10 .54499 /UIDB /05757 /2020) and UIDP/05757/2020) (DOI: 10.54499 /UIDB /05757 /2020) and SusTEC (LA/P/0007/2021) (DOI: 10.54499 /LA /P /0007 /2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMendes, João; Lima, José; Costa, Lino; Rodrigues, Nuno; Pereira, Ana I. (2024). Deep learning networks for olive cultivar identification: a comprehensive analysis of convolutional neural networks. Smart Agricultural Technology. ISSN 2772-3755. 8, p. 1-13pt_PT
dc.identifier.doi10.1016/j.atech.2024.100470pt_PT
dc.identifier.issn2772-3755
dc.identifier.urihttp://hdl.handle.net/10198/29887
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationLA/P/0007/2021pt_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCNNspt_PT
dc.subjectConvolutional neural networkspt_PT
dc.subjectCultivar identificationpt_PT
dc.subjectImage-based identificationpt_PT
dc.subjectOlive leavespt_PT
dc.subjectPrecision agriculturept_PT
dc.titleDeep learning networks for olive cultivar identification: a comprehensive analysis of convolutional neural networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
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.citation.endPage13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleSmart Agricultural Technologypt_PT
oaire.citation.volume8pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMendes
person.familyNameLima
person.familyNameRodrigues
person.familyNamePereira
person.givenNameJoão
person.givenNameJosé
person.givenNameNuno
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.orcid0000-0003-3803-2043
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.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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