Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture

The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction...

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Detalles Bibliográficos
Autores: Cáceres Rodríguez, Gabriela Belén, Millán Gata, Pablo, Pereira Martín, Mario, Lozano, David
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/4481
Acceso en línea:https://hdl.handle.net/20.500.12412/4481
Access Level:acceso abierto
Palabra clave:Sustainability
Moisture sensor
Economic optimization
Crop yield
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spelling Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in AgricultureCáceres Rodríguez, Gabriela BelénMillán Gata, PabloPereira Martín, MarioLozano, DavidSustainabilityMoisture sensorEconomic optimizationCrop yieldThe growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of the controller is to find an irrigation technique that optimizes water and energy consumption while ensuring adequate levels of soil moisture for crops, achieving the maximum crop yield. For this purpose, the developed predictive controller makes use of soil moisture data at different depths, and it formulates a constrained optimization problem that considers energy and water costs, crop transpiration, and an accurate dynamical nonlinear model of the water dynamics in the soil, reflecting the reality. This controller strategy is compared with a classical irrigation strategy adopted by a human expert in a specific case study, demonstrating that it is possible to obtain significant reductions in water and energy consumption without compromising crop yields.2021info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/4481reponame:Brújulainstname:Universidad Loyola AndalucíaInglésThe authors wish to acknowledge the contribution of Pedro Gavilán as leader of the INNOREGA project (PP.AVA.AVA2019.024).http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/44812026-06-24T12:48:37Z
dc.title.none.fl_str_mv Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
title Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
spellingShingle Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
Cáceres Rodríguez, Gabriela Belén
Sustainability
Moisture sensor
Economic optimization
Crop yield
title_short Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
title_full Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
title_fullStr Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
title_full_unstemmed Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
title_sort Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
dc.creator.none.fl_str_mv Cáceres Rodríguez, Gabriela Belén
Millán Gata, Pablo
Pereira Martín, Mario
Lozano, David
author Cáceres Rodríguez, Gabriela Belén
author_facet Cáceres Rodríguez, Gabriela Belén
Millán Gata, Pablo
Pereira Martín, Mario
Lozano, David
author_role author
author2 Millán Gata, Pablo
Pereira Martín, Mario
Lozano, David
author2_role author
author
author
dc.subject.none.fl_str_mv Sustainability
Moisture sensor
Economic optimization
Crop yield
topic Sustainability
Moisture sensor
Economic optimization
Crop yield
description The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of the controller is to find an irrigation technique that optimizes water and energy consumption while ensuring adequate levels of soil moisture for crops, achieving the maximum crop yield. For this purpose, the developed predictive controller makes use of soil moisture data at different depths, and it formulates a constrained optimization problem that considers energy and water costs, crop transpiration, and an accurate dynamical nonlinear model of the water dynamics in the soil, reflecting the reality. This controller strategy is compared with a classical irrigation strategy adopted by a human expert in a specific case study, demonstrating that it is possible to obtain significant reductions in water and energy consumption without compromising crop yields.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/4481
url https://hdl.handle.net/20.500.12412/4481
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The authors wish to acknowledge the contribution of Pedro Gavilán as leader of the INNOREGA project (PP.AVA.AVA2019.024).
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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