Intelligent Approaches for Optimal Irrigation Management: A Comprehensive Review

The complexity of irrigation management necessitates the use of professional experience for effective decision-making. Expert systems (ES) are specifically engineered to mimic human expertise efficiently, allowing cognitive mechanisms to be replicated at the irrigation level. The value of combining human knowledge with cutting-edge technologies such as fog computing, cloud computing, blockchain, IoT, and machine learning has recently come to be recognized more and more. The objective is to create an integrated platform that increases irrigation systems’ resilience and adaptability while also increasing their accuracy, efficiency, and capacity for adaptive decision-making in light of agricultural environments’ changing conditions. With a focus on ES, fuzzy logic, and machine learning algorithms, this paper examines the research trends and applicability of intelligent approaches for optimal irrigation management scenarios. It also provides a thorough analysis of how these approaches contribute to the overall success of smart irrigation management.