SMART IRRIGATION SCHEDULING USING SOIL MOISTURE AND MICROCLIMATE SENSOR DATA

Abstract

Smart irrigation is a critical advancement in modern agriculture, aimed at optimizing water usage while ensuring maximum crop productivity. Traditional irrigation methods often rely on fixed schedules or manual observation, which can lead to water wastage, inefficient resource utilization, and inconsistent crop growth. This project proposes a smart irrigation scheduling system that leverages real-time soil moisture and microclimate sensor data to automate and optimize irrigation processes. The system utilizes various sensors to continuously monitor key environmental parameters such as soil moisture levels, temperature, humidity, and atmospheric conditions. These inputs are analyzed using machine learning algorithms and threshold-based decision models to determine the precise water requirements of crops. By integrating data-driven decision-making, the system ensures that irrigation is applied only when necessary, reducing water consumption and improving efficiency. Advanced predictive techniques are incorporated to forecast irrigation needs based on historical and real-time data, enabling proactive water management. The system adapts to changing environmental conditions and crop requirements, ensuring consistent and sustainable agricultural practices. This adaptive capability distinguishes it from conventional irrigation systems, which often lack responsiveness to dynamic environmental factors.