Economic model predictive control for smart and sustainable agriculture

Abstract

The joint effects of rise of global population, climate change and water scarcity makes the shift towards an efficient and sustainable agriculture more and more urgent. Fortunately, recent developments in low-cost, IoT-based sensors and actuators can help us to incorporate advanced control techniques for efficient irrigation system. This paper proposes the use of an economic model predictive control at a farm scale. The controller makes use of soil moisture data sent by the sensors, price signals, operative restrictions, and accurate dynamical models of water dynamics in the soil. Its performance is demonstrated through simulations based on a real case-study, showing that it is possible to obtain significant reductions in water and energy consumption and operation costs.

This research was partially funded by the proyect INNOREGA: Riego sostenible basado en monitorización distribuida e inteligencia artificial (PP.AVA.AVA2019.024). This project is co-financed by the FEDER funds (UE) within the “Programa Operativo de Andalucía 2014–2020”.