Título Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture
Publicación externa No
Medio Agronomy
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 3.94900
Impacto SJR 0.65400
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116690562&doi=10.3390%2fagronomy11091810&partnerID=40&md5=1bbaec84423aa9abee8b9535d8518fa6
Fecha de publicacion 01/09/2021
ISI 000699257200001
Scopus Id 2-s2.0-85116690562
DOI 10.3390/agronomy11091810
Abstract 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.
Palabras clave sustainability; moisture sensor; economic optimization; crop yield
Miembros de la Universidad Loyola

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