A Crop-Intelligence Driven IoT Framework for Precision Irrigation: Review, Gaps, and Future Design Directions
DOI:
https://doi.org/10.66635/xhbzhn51Keywords:
Smart irrigation, Internet of Things (IoT), Precision agriculture, Artificial intelligence, Decision support systems, Crop water management, Sustainable agricultureAbstract
The rapid expansion of IoT-enabled irrigation technologies has significantly improved on-farm monitoring; however, most existing systems remain limited to threshold-based or descriptive control and lack crop-intelligent, analytically grounded decision-making capabilities. This paper presents a structured review of IoT-based precision irrigation systems, AI-driven decision-support approaches, and their associated limitations, highlighting critical gaps related to intelligence, data integration, decision architecture, scalability, affordability, and long-term validation. Building on these gaps, the study proposes a novel conceptual IoT framework for precision irrigation that integrates soil moisture sensing, climatic and evapotranspiration analytics, and crop growth-stage intelligence within a hybrid rule-based and AI-driven decision architecture. The framework is designed to enable adaptive, explainable, and rational irrigation decisions while remaining practical for real-world deployment, particularly in developing and smallholder-dominated agricultural contexts. Through logical and conceptual validation, the proposed design demonstrates clear advantages over moisture-only systems in terms of water-use efficiency, energy conservation, and yield reliability. The study contributes a scientifically grounded and deployment-oriented perspective to smart irrigation research, offering a strategic pathway toward sustainable agricultural water management.
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