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Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning Techniques

2024Conference paper Restricted access
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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorMendes, João
dc.contributor.authorMoso, Juliet
dc.contributor.authorBerger, Guido
dc.contributor.authorLima, José
dc.contributor.authorCosta, Lino
dc.contributor.authorGuessoum, Zahia
dc.contributor.authorPereira, Ana I.
dc.date.accessioned2025-04-23T10:22:56Z
dc.date.available2025-04-23T10:22:56Z
dc.date.issued2024
dc.description.abstractOlive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.eng
dc.description.sponsorshipThe cooperation was supported by the HORIZON-WIDERA- 2021-ACCESS-03-01 STEP - STEM Research and Equality, Diversity and Inclusion Project, under Grant Agreement No. 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 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021).
dc.identifier.citationMendes, João; Moso, Juliet; Szekir Berger, Guido; Lima, José; Costa, Lino; Guessoum, Zahia; Pereira, Ana I. (2024). Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning Techniques. In Optimization, Learning Algorithms and Applications (OL2A 2024), Part I. Cham: Springer Nature. p. 157- 172. eISBN 978-3-031-77426-3
dc.identifier.doi10.1007/978-3-031-77426-3_11
dc.identifier.isbn9783031774256
dc.identifier.isbn9783031774263
dc.identifier.urihttp://hdl.handle.net/10198/34427
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.relationALGORITMI Research Center
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.subjectOptimization
dc.subjectHybrid Model
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectOlive Disease
dc.subjectOlive Leaves
dc.titleOptimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning Techniqueseng
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.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.citation.endPage172
oaire.citation.startPage157
oaire.citation.title4th International Conference on Optimization, Learning Algorithms and Applications, OL2A 2024
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
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person.familyNameBerger
person.familyNameLima
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person.givenNameJoão
person.givenNameGuido
person.givenNameJosé
person.givenNameAna I.
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person.identifier.orcid0000-0003-0979-8314
person.identifier.orcid0000-0002-4100-1494
person.identifier.orcid0000-0001-7902-1207
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-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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