<![CDATA[The rapid adoption of Internet of Things (IoT) technologies has accelerated the development of smart agriculture systems. However, existing studies predominantly focus on single-resource optimization and lack integrated artificial intelligence (AI) approaches within distributed architectures, resulting in suboptimal system-wide performance. This study proposes an AI-driven energy-efficient IoT framework that integrates the Random Forest algorithm with an edge–fog–cloud computing architecture to enable holistic multi-resource optimization. A quantitative simulation-based approach was employed using soil moisture data from the NASA SMAP dataset, with a case study in Magelang, Indonesia. The system was evaluated using key performance metrics, including energy consumption, network latency, packet delivery ratio (PDR), and water usage efficiency. The results demonstrate significant improvements, including a 28.65% reduction in energy consumption, a 31.43% decrease in latency, an increase in PDR to 96.8%, and a 20.3% improvement in water usage efficiency. Statistical validation confirms that these improvements are significant (p < 0.05). The main contribution of this study lies in the development of a holistic AI-driven IoT framework that simultaneously optimizes energy, water, computation, and communication without trade-offs. The proposed approach offers a scalable, adaptive, and efficient solution for real-world smart agriculture systems.]]>
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