Pulse-based, Periodic MPC for Irrigation in Smart and Sustainable Agriculture

The growing population, together with global warming and the difficulty of accessing water, makes the increase of efficient and sustainable agriculture a priority. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators presents great opportunities in this direction, since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale. This paper proposes a pulse-based, periodic, economic predictive controller. Its goal is to find the irrigation pulse trains that optimize water and energy consumption while ensuring adequate levels of soil moisture for the crops. For this purpose, the developed MPC makes use of soil moisture data at different depths, sent by a set of field sensors, and formulates a constrained optimization problem that takes into account water costs, electricity prices, and an accurate dynamical nonlinear agro-hydrological model. Its performance is tested by simulating real case studies, which show that water and energy consumption can be significantly reduced.