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dc.contributor.authorCama-Pinto, Dora
dc.contributor.authorDamas, Miguel
dc.contributor.authorHolgado-Terriza, Juan Antonio
dc.contributor.authorArrabal Campos, Francisco Manuel 
dc.contributor.authorMartínez Lao, Juan Antonio 
dc.contributor.authorCama-Pinto, Alejandro
dc.contributor.authorManzano Agugliaro, Francisco Rogelio 
dc.date.accessioned2023-01-25T18:19:49Z
dc.date.available2023-01-25T18:19:49Z
dc.date.issued2023-01-13
dc.identifier.issn2073-4395
dc.identifier.urihttp://hdl.handle.net/10835/14178
dc.description.abstractThe production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the technology of wireless sensor networks (WSN) in the monitoring of agronomic parameters. Therefore, prior planning of the deployment of WSN nodes is relevant because their coverage decreases when the radio waves are attenuated by the foliage of the plantation. In that sense, the method proposed in this study applies Deep Learning to develop an empirical model of radio wave attenuation when it crosses vegetation that includes height and distance between the transceivers of the WSN nodes. The model quality is expressed via the parameters cross-validation, R2 of 0.966, while its generalized error is 0.920 verifying the reliability of the empirical model.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectdeep learninges_ES
dc.subjectneural networkes_ES
dc.subjectprecision agriculturees_ES
dc.subjectpropagation modeles_ES
dc.subjectwireless sensor networkses_ES
dc.titleA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouseses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2073-4395/13/1/244es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/agronomy13010244


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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