Enhancing Smart and Sustainable Agriculture through Model Predictive Control Design and Implementation

Abstract

Agriculture plays a critical role in economic growth and food security, yet it consumes an outsized 70% of the world’s freshwater resources to irrigate just a quarter of its croplands. Inefficient water management not only wastes this valuable resource but also significantly reduces crop yields. Additionally, this practice limits water availability for other essential sectors. Conversely, some countries leverage renewable resources to offset energy costs. However, in many developing nations, limited or no access to energy severely impacts farm productivity. Hence, it is crucial to understand mechanisms that optimize water and energy management, boost agricultural productivity, and conserve resources. Precision agriculture aligns resource management with crop needs, aiming for sustainable production. The main goals of precision agriculture are the improvement of water efficiency, the reduction of energy consumption, and the maximization of crop productivity. Smart irrigation, a crucial component of precision agriculture, involves the precise application of water at the right time, in the right quantities, and at the right locations within the field. This enables farmers to conserve valuable resources while safeguarding crop growth. To comply with the above-mentioned, smart irrigation relies on monitoring technologies such as Wireless Sensor Networks (WSN) and employs control strategies that take into account critical parameters like soil moisture levels, weather patterns, and other factors crucial for crop growth. One of the main objectives of this thesis is the design and development of an economic periodic model predictive controllers that makes use of a dynamic non-linear agro-hydrological model, taking into account the Volumetric Water Content (VWC) at various soil depths. These controllers are aimed at determining irrigation strategies that optimize water and energy consumption while ensuring optimal levels of VWC for crops, thereby maximizing crop yields. Within the scope of this objective, several approaches were explored. Initially, an MPC (Model Predictive Control) was developed using analog control actions to approximate the behavior of real irrigation valves. Subsequently, binary control actions were integrated to closely mimic on-off valve irrigation systems. An additional approach involved the integration of renewable energy sources, such as microgrids, with water reservoirs and crop fields to achieve optimal management of both water and energy resources. Finally, the economic periodic model predictive controller with binary control actions, integrated with a monitoring system, was implemented on an organic farm in Seville. The contribution of this thesis is fundamentally practical within the engineering domain. It demonstrates not only the effectiveness of the controller in simulation but also the successful integration of both monitoring and control systems in a real-world agricultural context.