Abstract Efficient irrigation management is critical for improving water use efficiency, increasing crop productivity, and ensuring long-term sustainability in agriculture. This study presents an Internet of Things (IoT)-enabled fuzzy logic irrigation control system that adaptively adjusts irrigation schedules based on real-time soil moisture, ambient temperature, and humidity data. The fuzzy inference engine, designed with expert knowledge and implemented using Mamdani-type reasoning, was benchmarked against conventional fixed-schedule irrigation and an AI threshold-based control system across multiple soil types and environmental conditions. Experimental results demonstrate that the proposed system reduced water consumption by 31.4% compared with conventional methodsand by 12.7% compared with threshold-based AI, while achieving a 22.8% increase in average crop yieldand a system reliability of 98.6%. Statistical validation using one-way ANOVA and Tukey’s HSD confirmed the significance of these improvements (p < 0.05). Beyond efficiency gains, the fuzzy logic approach proved effective in handling sensor noise, mitigating uncertainty, and providing smooth, adaptive control actions without abrupt fluctuations. Furthermore, the modular design of the system facilitates integration with adaptive neuro-fuzzy inference systems (ANFIS), enabling self-learning and continuous optimization. These findings highlight the potential of IoT-driven fuzzy systems as scalable, cost-effective solutions for precision agriculture and sustainable water resource management.
Adewumi et al. (Tue,) studied this question.
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