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dc.contributor.author | Guillén-Navarro, Miguel A. | es_ES |
dc.contributor.author | Martínez-España, Raquel | es_ES |
dc.contributor.author | Llanes, Antonio | es_ES |
dc.contributor.author | Bueno-Crespo, Andrés | es_ES |
dc.contributor.author | Cecilia-Canales, José María | es_ES |
dc.date.accessioned | 2021-07-27T03:37:33Z | |
dc.date.available | 2021-07-27T03:37:33Z | |
dc.date.issued | 2020 | es_ES |
dc.identifier.issn | 1876-1364 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/170274 | |
dc.description.abstract | [EN] Deep learning techniques provide a novel framework for prediction and classification in decision-making procedures that are widely applied in different fields. Precision agriculture is one of these fields where the use of decision-making technologies provides better production with better costs and a greater benefit for farmers. This paper develops an intelligent framework based on a deep learning model for early prediction of crop frost to help farmers activate anti-frost techniques to save the crop. This model is based on a long short-term memory (LSTM) model and it is designed to predict low temperatures. The model is based on information from an IoT infrastructure deployed on two plots in Murcia (Southeast of Spain). Three experiments are performed; a cross validation to validate the model from the most pessimistic point of view, a validation of 24 consecutive hours of temperatures, in order to know 24 hours before the possible temperature drop and a comparison with two traditional time series prediction techniques, namely Auto Regressive Integrated Moving Average and the Gaussian process. The results obtained are satisfactory, being better the results of the LSTM, obtaining an average quadratic error of less than a Celsius degree and a determination coefficient R-2 greater than 0.95. | es_ES |
dc.description.sponsorship | This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IOS Press | es_ES |
dc.relation.ispartof | Journal of Ambient Intelligence and Smart Environments | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | LSTM | es_ES |
dc.subject | Precision agriculture | es_ES |
dc.subject | IoT | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | A deep learning model to predict lower temperatures in agriculture | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3233/AIS-200546 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2016-78799-P/ES/DESARROLLO HOLISTICO DE APLICACIONES EMERGENTES EN SISTEMAS HETEROGENEOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//RYC-2018-025580-I/ | es_ES |
dc.rights.accessRights | Abierto | 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 | Guillén-Navarro, MA.; Martínez-España, R.; Llanes, A.; Bueno-Crespo, A.; Cecilia-Canales, JM. (2020). A deep learning model to predict lower temperatures in agriculture. Journal of Ambient Intelligence and Smart Environments. 12(1):21-34. https://doi.org/10.3233/AIS-200546 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3233/AIS-200546 | es_ES |
dc.description.upvformatpinicio | 21 | es_ES |
dc.description.upvformatpfin | 34 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.pasarela | S\404367 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia | es_ES |
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