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FARMIT: Continuous Assessment of Crop Quality Using Machine Learning and Deep Learning Techniques for IoT-based Smart Farming

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dc.contributor.authorPerales Gómez, Ángel Luis
dc.contributor.authorLópez de Teruel Alcolea, Pedro Enrique
dc.contributor.authorRuiz García, Alberto
dc.contributor.authorGarcía Mateos, Ginés
dc.contributor.authorGarcía Clemente, Félix Jesús
dc.contributor.departmentIngeniería y Tecnología de Computadores
dc.date.accessioned2024-02-07T13:27:32Z
dc.date.available2024-02-07T13:27:32Z
dc.date.issued2022-03-31
dc.description©<2022>. This manuscript version is made available under the CC-BY license http://creativecommons.org/licenses/ccby/4.0/ This document is the Acepted version of a Published Work that appeared in final form in [Cluster Computing]. To access the final edited and published work see [https://doi.org/ 10.1007/s10586-021-03489-9]
dc.description.abstractThe race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things (IoT) technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this work, we fill a gap in the literature and present a novel architecture based on IoT and Machine Learning / Deep Learning technologies or the continuous assessment of agricultural crop quality. This architecture is divided into three layers that work together to gather, process, and analyze data from different sources to evaluate crop quality. In the experiments, he proposed approach based on data aggregation from different sources reaches a lower percentage error than considering only one source. In particular, the percentage error achieved by our approach in the test dataset was 6.59, while the percentage error achieved exclusively using data from sensors was 6.71.es
dc.formatapplication/pdfes
dc.format.extent16 páginases
dc.identifier.citationCluster Computing, Volume 25, pages 2163–2178, (2022)
dc.identifier.doihttps://doi.org/10.1007/s10586-021-03489-9
dc.identifier.issnPrint: 1386-7857
dc.identifier.issnElectrónico: 1573-7543
dc.identifier.urihttp://hdl.handle.net/10201/138919
dc.languageenges
dc.publisherSpringeres
dc.relationOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been funded by Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI), FEDER funds, under Grants RTI2018-095855-B-I00 and RTI2018-098156-B-C53.es
dc.relation.publisherversionhttps://link.springer.com/content/pdf/10.1007/s10586-021-03489-9.pdfes
dc.relation.replaceshttps://link.springer.com/content/pdf/10.1007/s10586-021-03489-9.pdfes
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectCrop qualityes
dc.subjectDeep learning
dc.subjectInternet of things
dc.subjectMachine learning
dc.subjectSmart farming
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes
dc.titleFARMIT: Continuous Assessment of Crop Quality Using Machine Learning and Deep Learning Techniques for IoT-based Smart Farminges
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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