Abstract
Sustainable agriculture requires efficient irrigation management, especially in the face of unpredictable weather and soil conditions. The unpredictability of the environment frequently causes conventional irrigation systems to use water inefficiently. In this work, a hybrid artificial neural network (ANN)-fuzzy logic model that combines fuzzy decision-making and predictive learning is presented for intelligent irrigation control. The fuzzy logic system optimises irrigation in the face of uncertainty, while the artificial neural network (ANN) component forecasts soil moisture and water demand. The model improves irrigation precision by using real-time sensor data on soil and weather characteristics based on the Internet of Things. According to experimental results, the suggested system outperforms current models with 94.7% accuracy, 94.1% precision, 93.8% recall, and an RMSE of 0.057. This hybrid framework encourages smart, sustainable farming methods and effective water use.