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Machine Learning Applications in Agriculture

2023-12-01Recensão Acesso aberto
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dc.contributor.authorAraújo, Sara Oleiro
dc.contributor.authorPeres, Ricardo Silva
dc.contributor.authorRamalho, José Cochicho
dc.contributor.authorLidon, Fernando
dc.contributor.authorBarata, José
dc.contributor.institutionDCT - Departamento de Ciências da Terra
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.institutionGeoBioTec - Geobiociências, Geoengenharias e Geotecnologias
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2024-02-22T23:53:40Z
dc.date.available2024-02-22T23:53:40Z
dc.date.issued2023-12-01
dc.descriptionThis work was supported in part by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the research units UNINOVA-CTS (UIDB/00066/2020), GeoBioTec (UIDP/04035/2020), CEF (UIDB/00239/2020), and the Associate Laboratory TERRA (LA/P/0092/2020). Publisher Copyright: © 2023 by the authors.
dc.description.abstractProgress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent27
dc.format.extent810758
dc.identifier.doi10.3390/agronomy13122976
dc.identifier.issn2073-4395
dc.identifier.otherPURE: 83894596
dc.identifier.otherPURE UUID: 50f4eae7-3155-44ca-982b-120c926180d8
dc.identifier.otherScopus: 85180676919
dc.identifier.otherWOS: 001131493500001
dc.identifier.urihttp://hdl.handle.net/10362/163989
dc.identifier.urlhttps://www.scopus.com/pages/publications/85180676919
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04035%2F2020/PT
dc.relationGeoBioSciences GeoTechnologies and GeoEngineering
dc.relationForest Research Centre
dc.subjectAgriculture 4.0
dc.subjectmachine learning
dc.subjectPRISMA
dc.subjectsystematic reviews and meta analytics
dc.subjectAgronomy and Crop Science
dc.subjectSDG 2 - Zero Hunger
dc.subjectSDG 8 - Decent Work and Economic Growth
dc.titleMachine Learning Applications in Agricultureen
dc.title.subtitleCurrent Trends, Challenges, and Future Perspectivesen
dc.typereview
degois.publication.issue12
degois.publication.titleAgronomy
degois.publication.volume13
dspace.entity.typePublication
oaire.awardNumberUIDP/04035/2020
oaire.awardNumberUIDB/00239/2020
oaire.awardTitleGeoBioSciences GeoTechnologies and GeoEngineering
oaire.awardTitleForest Research Centre
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04035%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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.rightsopenAccess
relation.isProjectOfPublication5875cdfc-db95-4f41-81aa-2e223d60beba
relation.isProjectOfPublication3e4e8c15-b7bc-4b0f-b32a-764022bf66d2
relation.isProjectOfPublication.latestForDiscovery5875cdfc-db95-4f41-81aa-2e223d60beba

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