A decision support system for water optimization in anti-frost techniques by sprinklers

Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of tempe...

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Detalhes bibliográficos
Autores: Guillén Navarro, Miguel Ángel, Martínez España, Raquel, Bueno Crespo, Andrés, Morales García, Juan, López Ayuso, Belén, Cecilia Canales, José María
Formato: artículo
Fecha de publicación:2020
País:España
Recursos:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/7395
Acesso em linha:http://hdl.handle.net/10952/7395
Access Level:acceso abierto
Palavra-chave:Mutivariate LSTM based approach
IoT system
Intelligent Systems
Precision Agriculture
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spelling A decision support system for water optimization in anti-frost techniques by sprinklersGuillén Navarro, Miguel ÁngelMartínez España, RaquelBueno Crespo, AndrésMorales García, JuanLópez Ayuso, BelénCecilia Canales, José MaríaMutivariate LSTM based approachIoT systemIntelligent SystemsPrecision AgriculturePrecision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 ºC in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 ºC and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.Ingeniería, Industria y Construcción2020info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/7395reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/73952026-06-07T18:35:21Z
dc.title.none.fl_str_mv A decision support system for water optimization in anti-frost techniques by sprinklers
title A decision support system for water optimization in anti-frost techniques by sprinklers
spellingShingle A decision support system for water optimization in anti-frost techniques by sprinklers
Guillén Navarro, Miguel Ángel
Mutivariate LSTM based approach
IoT system
Intelligent Systems
Precision Agriculture
title_short A decision support system for water optimization in anti-frost techniques by sprinklers
title_full A decision support system for water optimization in anti-frost techniques by sprinklers
title_fullStr A decision support system for water optimization in anti-frost techniques by sprinklers
title_full_unstemmed A decision support system for water optimization in anti-frost techniques by sprinklers
title_sort A decision support system for water optimization in anti-frost techniques by sprinklers
dc.creator.none.fl_str_mv Guillén Navarro, Miguel Ángel
Martínez España, Raquel
Bueno Crespo, Andrés
Morales García, Juan
López Ayuso, Belén
Cecilia Canales, José María
author Guillén Navarro, Miguel Ángel
author_facet Guillén Navarro, Miguel Ángel
Martínez España, Raquel
Bueno Crespo, Andrés
Morales García, Juan
López Ayuso, Belén
Cecilia Canales, José María
author_role author
author2 Martínez España, Raquel
Bueno Crespo, Andrés
Morales García, Juan
López Ayuso, Belén
Cecilia Canales, José María
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Mutivariate LSTM based approach
IoT system
Intelligent Systems
Precision Agriculture
topic Mutivariate LSTM based approach
IoT system
Intelligent Systems
Precision Agriculture
description Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 ºC in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 ºC and a coefficient of determination R2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/7395
url http://hdl.handle.net/10952/7395
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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