Marine data analytics: Machine learning algorithms to optimize seaweed growth

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dc.contributor.authorMongelli, Francescaen
dc.contributor.authorMenolotto, Matteoen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorDemarchi, Daniloen
dc.contributor.editorLarcher, L.en
dc.contributor.funderHorizon 2020en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-02-29T11:32:54Z
dc.date.available2024-02-29T11:32:54Z
dc.date.issued2024-01-17en
dc.description.abstractAquaculture farming faces challenges to increase production while maintaining welfare of livestock, efficiently use of resources, and being environmentally sustainable. To help overcome these challenges, remote and real-time monitoring of the environmental and biological conditions of the aquaculture site is highly important. Multiple remote monitoring solutions for investigating the growth of seaweed are available, but no integrated solution that monitors different biotic and abiotic factors exists. A new integrated multi-sensing system would reduce the cost and time required to deploy the system and provide useful information on the dynamic forces affecting the plants and the associated biomass of the harvest. As part of the EU funded IMPAQT project a new multi modal seaweed sensing system was developed incorporating a variety of sensor to investigate Seaweed growth parameters. The growth rate of seaweed is significantly affected by wave parameters and sea conditions. The wave characteristics in an aquaculture farm are normally measured using expensive equipment, which is not affordable for many farmers or researchers, and is not easily relocated from place to place to evaluate wave conditions in a variety of locations. This research focuses on developing an artificial neural network that can estimate wave height using acceleration and angular velocity data recorded by a low cost IMU sensor.en
dc.description.sponsorshipScience Foundation Ireland (13/RC/2077 P2)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Flynn, B., Campion, O., Peres, C. and Emann, M. (2024) 'Marine data analytics: Machine learning algorithms to optimize seaweed growth', 2023 Smart Systems Integration Conference and Exhibition (SSI), Brugge, Belgium, 28-30 March, pp. 1-6. https://doi.org/10.1109/SSI58917.2023.10387961en
dc.identifier.doi10.1109/SSI58917.2023.10387961en
dc.identifier.endpage6en
dc.identifier.isbn979-8-3503-2506-5en
dc.identifier.isbn979-8-3503-0231-8en
dc.identifier.issued2en
dc.identifier.journaltitleIEEE Electron Device Lettersen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15606
dc.identifier.volume45en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2023 Smart Systems Integration Conference and Exhibition (SSI), Brugge, Belgium, 28-30 March 2023en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/774109/EU/Intelligent management system for integrated multi-trophic aquaculture/IMPAQTen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.rights© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectAquacultureen
dc.subjectIntegrated multi-trophic aquacultureen
dc.subjectArtificial neural networken
dc.subjectWave characteristicsen
dc.subjectIMUen
dc.titleMarine data analytics: Machine learning algorithms to optimize seaweed growthen
dc.typeConference itemen
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