Integrating AI-based Methods for Enhanced the Performance of Intelligent Irrigation Systems

Water management is a critical issue affecting agriculture, necessitating efficient water management practices to ensure sustainable crop production. Traditional irrigation methods often lead to overuse or underuse of water, resulting in suboptimal crop growth and wastage of valuable resources. This article proposes an advanced architecture for intelligent irrigation systems that integrates Fuzzy Logic (FL) and Deep Learning (DL). DL algorithms are employed for plant type identification and prediction, and FL is used to estimate the optimal watering duration. The integration of these AI algorithms with sensor networks and real-time data analytics aims to optimize water usage and improve plant yield. The proposed system’s design, implementation, and potential benefits are discussed, demonstrating significant improvements over traditional.