A Systematic Literature Review of IoT- and AI-Based Intelligent Irrigation Systems for Water Optimization in Precision Agriculture

Abstract

The increasing global demand for water has emphasized the importance of irrigation systems capable of efficiently managing water use in precision agriculture. This paper presents a review of one hundred studies published between 2018 and 2025 that address the application of Internet of Things (IoT) technologies and computational approaches in irrigation management. The reviewed literature describes the use of sensor networks, wireless communication systems data-driven control mechanisms and automated decision-support tools aimed at improving irrigation practices while supporting crop production. The studies are organized according to the technologies and methods employed, including sensor-based data acquisition IoT-enabled monitoring platforms and intelligent irrigation control systems. A hybrid classification is also introduced to describe approaches that combine predictive modelling with adaptive irrigation strategies. Additionally, the reviewed works are discussed with respect to several system-related aspects such as water management objectives system architecture, and application scale. The paper also summarizes commonly used datasets and evaluation metrics reported in the literature and outlines key challenges identified by previous studies including data quality, communication reliability scalability and operational costs. Finally, the review highlights research directions that may contribute to the development of more efficient reliable and sustainable irrigation systems for precision agriculture