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Intelligent monitoring and management platform for the prevention of olive pests and diseases, including IoT with sensing, georeferencing and image acquisition capabilities through computer vision

dc.contributor.authorAlves, Adília
dc.contributor.authorMorais, A. Jorge
dc.contributor.authorFilipe, Vítor
dc.contributor.authorPereira, José
dc.date.accessioned2023-03-23T14:32:10Z
dc.date.available2023-03-23T14:32:10Z
dc.date.issued2022
dc.description.abstractClimate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-86887-1_23pt_PT
dc.identifier.isbn978-3-030-86887-1
dc.identifier.urihttp://hdl.handle.net/10400.2/13521
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectOlives sustainable productionpt_PT
dc.subjectInternet of Thingspt_PT
dc.subjectConvolutional neural networkpt_PT
dc.subjectDeep learningpt_PT
dc.subjectComputer visionpt_PT
dc.titleIntelligent monitoring and management platform for the prevention of olive pests and diseases, including IoT with sensing, georeferencing and image acquisition capabilities through computer visionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage213pt_PT
oaire.citation.startPage210pt_PT
oaire.citation.titleDCAI 2021: Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions. 18th International Conferencept_PT
oaire.citation.volume332pt_PT
person.familyNameAlves
person.familyNameMorais
person.familyNameJesus Filipe
person.familyNameCardoso Pereira
person.givenNameAdília
person.givenNameA. Jorge
person.givenNameVítor Manuel
person.givenNameJosé Alberto
person.identifierD-1723-2009
person.identifier.ciencia-idF314-1D77-536E
person.identifier.ciencia-idE716-23C3-FAFF
person.identifier.ciencia-id611F-80B2-A7C1
person.identifier.orcid0000-0002-3792-1968
person.identifier.orcid0000-0003-2224-1609
person.identifier.orcid0000-0002-3747-6577
person.identifier.orcid0000-0002-2260-0600
person.identifier.scopus-author-id57194584599
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication571a1c49-329b-4b4e-ad48-78c5ff9c6e01
relation.isAuthorOfPublication1aa26598-8e13-4366-8183-eae03067003a
relation.isAuthorOfPublication0509f2aa-ab87-41b4-9c71-88fa8e59e19d
relation.isAuthorOfPublication.latestForDiscovery1aa26598-8e13-4366-8183-eae03067003a

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