Particle Swarm Optimization-Enhanced Fuzzy Control for Electrical Conductivity Regulation in Integrated Water–Fertilizer Irrigation Systems

Traditional water–fertilizer control systems often suffer from poor precision and slow response, limiting precision agriculture development. This study developed an electrical conductivity (EC) control system for water–fertilizer integration using a fuzzy Proportional-Integral-Derivative (PID) controller optimized by particle swarm optimization (PSO) and integrated with IoT technology. MATLAB/Simulink simulations showed the proposed controller achieved the smallest overshoot (7.64–8.15%), with average settling time reduced by 62.48 s and 20.38 s compared to conventional PID and fuzzy PID controllers, respectively (p < 0.001). Field experiments on winter wheat demonstrated a mean absolute EC deviation of 0.01125 ms/cm, with root-mean-square error (RMSE) of 0.0217 ms/cm, indicating high precision under field conditions. The system also maintained soil moisture in the optimal range (19–25%) with high irrigation uniformity (Christiansen’s coefficient Cu = 97.6%). The system maintained soil moisture in the optimal range (19–25%) while supporting stable soil nutrient levels and crop growth parameters. This study provides a validated technical solution for precision EC control while establishing a foundation for future fully integrated water–fertilizer management systems.