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• Developed an IoT-enabled smart agriculture system for automated irrigation and resource optimization. • Integrated real-time sensors for soil moisture, temperature, and humidity monitoring with Arduino-based automation. • Designed a predictive algorithm combining historical and real-time data to forecast irrigation needs with high accuracy (<5% variation). • Achieved a 30% reduction in water usage while maintaining optimal soil moisture and minimizing energy consumption (average 13.1 watts). • Demonstrated significant labor reduction and adaptability to arid and resource-constrained environments. This study presents an innovative smart agriculture system that integrates Internet of Things (IoT) technologies, predictive algorithms, and automated control mechanisms to optimize irrigation and enhance resource efficiency. The proposed solution leverages soil moisture, temperature, and humidity sensors connected to an Arduino-based microcontroller to automate irrigation based on real-time data. Farmers can remotely monitor and manage irrigation schedules through mobile devices, ensuring precision and convenience. The predictive algorithm combines historical and real-time data to forecast irrigation requirements, ensuring water is applied only when necessary. Field trials demonstrated a 30% reduction in water usage compared to traditional irrigation methods, while maintaining optimal soil moisture levels. Additionally, the system significantly reduced labor and energy costs, contributing to operational efficiency. By addressing water scarcity and promoting sustainable resource utilization, this smart system has the potential to improve crop yields, reduce greenhouse gas emissions, and advance ecological farming practices. In regions with limited water resources, the implementation of this technology represents a significant step toward sustainable agriculture, showcasing immense potential for broader adoption.
Gupta et al. (Wed,) studied this question.