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Evaluation of low-power devices for smart greenhouse development

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Evaluation of low-power devices for smart greenhouse development

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dc.contributor.author Morales-García, Juan es_ES
dc.contributor.author Bueno-Crespo, Andrés es_ES
dc.contributor.author Martínez-España, Raquel es_ES
dc.contributor.author Posadas-Yagüe, Juan-Luis es_ES
dc.contributor.author Manzoni, Pietro es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.date.accessioned 2023-12-18T19:05:22Z
dc.date.available 2023-12-18T19:05:22Z
dc.date.issued 2023-02 es_ES
dc.identifier.issn 0920-8542 es_ES
dc.identifier.uri http://hdl.handle.net/10251/200863
dc.description.abstract [EN] The combination of Artificial Intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental footprint. However, this convergence requires computational resources that enable the execution of AI workloads, and in the context of agriculture, ensuring autonomous operation and low energy consumption. In this work, we evaluate TinyML and edge computing platforms to predict the indoor temperature of an operational greenhouse in situ. In particular, the computational/energy trade-off of these platforms is assessed to analyse whether their use in this context is feasible. Two artificial neural networks are adapted to these platforms to predict the indoor temperature of the greenhouse. Our results show that the microcontroller-based devices can offer a competitive and energy-efficient computational alternative to more traditional edge computing approaches for lightweight ML workloads. es_ES
dc.description.sponsorship This work is derived from R & D projects RTC2019-007159-5, as well as the Ramon y Cajal Grant RYC2018-025580-I, funded by MCIN/AEI/10.13039/501100011033, "FSE invest in your future" and "ERDF A way of making Europe". es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof The Journal of Supercomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial Intelligence es_ES
dc.subject Edge computing es_ES
dc.subject Time series forecast es_ES
dc.subject TinyML es_ES
dc.subject CPU-GPU Performance es_ES
dc.subject Power consumption es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Evaluation of low-power devices for smart greenhouse development es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11227-023-05076-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//RTC-2019-007159-5//DESARROLLO DE INFRAESTRUCTURAS IOT DE ALTAS PRESTACIONES CONTRA EL CAMBIO CLIMÁTICO BASADAS EN INTELIGENCIA ARTIFICIAL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//RYC-2018-025580-I//AYUDA ADICIONAL RAMON Y CAJAL/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Morales-García, J.; Bueno-Crespo, A.; Martínez-España, R.; Posadas-Yagüe, J.; Manzoni, P.; Cecilia-Canales, JM. (2023). Evaluation of low-power devices for smart greenhouse development. The Journal of Supercomputing. 79:10277-10299. https://doi.org/10.1007/s11227-023-05076-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11227-023-05076-8 es_ES
dc.description.upvformatpinicio 10277 es_ES
dc.description.upvformatpfin 10299 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 79 es_ES
dc.relation.pasarela S\500876 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
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