A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses

The 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 param...

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Autores:
Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal Campos, Francisco Manuel
Martínez Lao, Juan Antonio
Cama-Pinto, Alejandro
Manzano-Agugliaro, Francisco
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9792
Acceso en línea:
https://hdl.handle.net/11323/9792
https://repositorio.cuc.edu.co/
Palabra clave:
Deep learning
Neural network
Precision agriculture
Propagation model
Wireless sensor networks
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_9345a6eeaecb47fdb10e7fb9c2675c43
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9792
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
title A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
spellingShingle A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
Deep learning
Neural network
Precision agriculture
Propagation model
Wireless sensor networks
title_short A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
title_full A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
title_fullStr A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
title_full_unstemmed A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
title_sort A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses
dc.creator.fl_str_mv Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal Campos, Francisco Manuel
Martínez Lao, Juan Antonio
Cama-Pinto, Alejandro
Manzano-Agugliaro, Francisco
dc.contributor.author.none.fl_str_mv Cama-Pinto, Dora
Damas, Miguel
Holgado-Terriza, Juan Antonio
Arrabal Campos, Francisco Manuel
Martínez Lao, Juan Antonio
Cama-Pinto, Alejandro
Manzano-Agugliaro, Francisco
dc.subject.proposal.eng.fl_str_mv Deep learning
Neural network
Precision agriculture
Propagation model
Wireless sensor networks
topic Deep learning
Neural network
Precision agriculture
Propagation model
Wireless sensor networks
description The 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.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-01-23T14:58:44Z
dc.date.available.none.fl_str_mv 2023-01-23T14:58:44Z
dc.date.issued.none.fl_str_mv 2023-01-13
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses. Agronomy 2023, 13, 244. https:// doi.org/10.3390/agronomy13010244
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/9792
dc.identifier.doi.none.fl_str_mv 10.3390/agronomy13010244
dc.identifier.eissn.spa.fl_str_mv 2073-4395
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses. Agronomy 2023, 13, 244. https:// doi.org/10.3390/agronomy13010244
10.3390/agronomy13010244
2073-4395
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9792
https://repositorio.cuc.edu.co/
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Agronomy
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spelling Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cama-Pinto, DoraDamas, MiguelHolgado-Terriza, Juan AntonioArrabal Campos, Francisco ManuelMartínez Lao, Juan AntonioCama-Pinto, AlejandroManzano-Agugliaro, Francisco2023-01-23T14:58:44Z2023-01-23T14:58:44Z2023-01-13Cama-Pinto, D.; Damas, M.; Holgado-Terriza, J.A.; Arrabal-Campos, F.M.; Martínez-Lao, J.A.; Cama-Pinto, A.; Manzano-Agugliaro, F. A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses. Agronomy 2023, 13, 244. https:// doi.org/10.3390/agronomy13010244https://hdl.handle.net/11323/979210.3390/agronomy130102442073-4395Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.16 páginasapplication/pdfengMDPI AGSwitzerlandhttps://www.mdpi.com/2073-4395/13/1/244A deep learning model of radio wave propagation for precision agriculture and sensor system in greenhousesArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Agronomy1. Bartkowiak, A.M. Energy-saving and low-emission livestock buildings in the concept of a smart farming. J. Water Land Dev. 2021, 51, 272–278. [CrossRef]2. Sagheer, A.; Mohammed, M.; Riad, K.; Alhajhoj, M. A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse Cultivation. Sensors 2021, 21, 223. [CrossRef] [PubMed]3. Akhigbe, B.; Munir, K.; Akinade, O.; Akanbi, L.; Oyedele, L. 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[CrossRef]161113Deep learningNeural networkPrecision agriculturePropagation modelWireless sensor networksPublicationORIGINALA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdfA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdfArtículoapplication/pdf5135890https://repositorio.cuc.edu.co/bitstreams/09ea0a11-a964-4202-b355-b60e4112e189/downloadd354f743666f2cf8133a87d2c93a57faMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/e17d8923-49aa-469a-a973-a23554450046/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdf.txtA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdf.txtExtracted texttext/plain68718https://repositorio.cuc.edu.co/bitstreams/cac71d59-2007-4c52-8dc6-4925e2092028/download778a819e395e6c445add0632e92dfe0cMD53THUMBNAILA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdf.jpgA Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses.pdf.jpgGenerated Thumbnailimage/jpeg16000https://repositorio.cuc.edu.co/bitstreams/5b98de1d-46aa-4d13-879d-4f4301bfa1ae/downloadf840a597822281201f409b13caebbc70MD5411323/9792oai:repositorio.cuc.edu.co:11323/97922024-09-17 11:09:53.036https://creativecommons.org/licenses/by/4.0/© 2023 by the authors. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.


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