Automatic Control Strategies in Fertigation Systems: A Technical Review

Fertigation, understood as the integration of nutrients into irrigation systems, requires precise automatic control to optimize the use of water and fertilizers. This review analyzes studies published between 2019 and 2024 that apply control strategies over four key variables: pH, Electrical Conductivity (EC), Irrigation Flow Rate (IFR), and Soil Moisture (SM). These variables are critical to the process due to their direct relationship with nutrient availability, root absorption, and water efficiency. This review examines classical techniques such as Proportional-Integral-Derivative (PID) controllers and advanced methods such as Model Predictive Control (MPC), along with modern approaches that incorporate Machine Learning (ML) algorithms and metaheuristic optimization tools, including Genetic Algorithms (GA) and bio-inspired methods like PSO. These strategies are evaluated based on performance metrics such as settling time, overshoot, and accuracy. Although intelligent approaches show improvements in simulated environments, their practical implementation still faces technical and economic challenges. This review presents a focused synthesis of the most effective techniques in automated fertigation and highlights opportunities for future research in real agricultural contexts.