The Performance of Water Irrigation Control using Fuzzy-GA Approach

Abstract

Irrigation in agriculture uses around 70% of freshwater resources globally, but traditional systems often result in ineffective utilization through rigid schedules or skewed decision-making. This article proposes an improved fuzzy logic controller developed using a Genetic Algorithm (GA) to optimize soil moisture control. The GA optimizes the fuzzy membership functions within 50 generations to enhance irrigation efficiency. Simulation and experimental results show that the fuzzy-GA controller maintained soil moisture at values close to the desired value of 25.1% with lower error rates, saving 858 mL more water than manual irrigation and 16 mL more than conventional fuzzy control. The results confirm the potential of fuzzy-GA systems in optimizing irrigation efficiency and ensuring sustainable use of water in agriculture. The fuzzy-genetic algorithm (Fuzzy-GA) improves fuzzy logic control by maintaining soil moisture at a target level of 25.1%, with a very low steady-state error of 0.03783%.