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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/11/9/1810 |
| _version_ | 1797520615490453504 |
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| author | Gabriela Cáceres Pablo Millán Mario Pereira David Lozano |
| author_facet | Gabriela Cáceres Pablo Millán Mario Pereira David Lozano |
| author_sort | Gabriela Cáceres |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T07:59:16Z |
| format | Article |
| id | doaj.art-a11b934a8a4e4faeb87a63fada899392 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T07:59:16Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-a11b934a8a4e4faeb87a63fada8993922023-11-22T11:38:42ZengMDPI AGAgronomy2073-43952021-09-01119181010.3390/agronomy11091810Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in AgricultureGabriela Cáceres0Pablo Millán1Mario Pereira2David Lozano3Departamento de Ingeniería, Universidad Loyola Andalucía, 41704 Dos Hermanas, SpainDepartamento de Ingeniería, Universidad Loyola Andalucía, 41704 Dos Hermanas, SpainDepartamento de Ingeniería, Universidad Loyola Andalucía, 41704 Dos Hermanas, SpainIFAPA Centro de Alameda del Obispo, Junta de Andalucía, 14004 Córdoba, SpainThe 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.https://www.mdpi.com/2073-4395/11/9/1810sustainabilitymoisture sensoreconomic optimizationcrop yield |
| spellingShingle | Gabriela Cáceres Pablo Millán Mario Pereira David Lozano Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture Agronomy sustainability moisture sensor economic optimization crop yield |
| title | 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_short | 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 |
| topic | sustainability moisture sensor economic optimization crop yield |
| url | https://www.mdpi.com/2073-4395/11/9/1810 |
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