Artificial intelligence of things for autonomous irrigation: A systematic review and system maturity model

Smart irrigation is a strategic lever for addressing global water scarcity and climate variability. The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has enabled the development of systems capable of real-time data collection, predictive analytics, and automated water management. This article presents a systematic review of 53 peer-reviewed studies (2020–2025) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines. We classify existing approaches through a technical taxonomy structured around five dimensions: system infrastructure, AI paradigms, communication protocols, energy management, and cybersecurity. The analysis reveals a dominance of cloud-centric architectures and deep learning, alongside a critical lack of transparency in energy and communication profiling. Notably, 67.9% of studies fail to address cybersecurity, highlighting a major blind spot in current research. To bridge these gaps, we propose the Smart Agriculture Integrated System Maturity (SA-ISM) model, a multi-layered framework designed to assess and improve the operational resilience, energy autonomy, and security of smart irrigation systems. This model serves as a roadmap for designing the next generation of autonomous, sustainable, and secure agricultural water management solutions.