Low-cost smart irrigation system based on soil moisture sensing

Abstract

This study presents a comprehensive scientific framework for the design and implementation of a low-cost embedded irrigation control system based on soil moisture sensing, proposed as a practical and analytically grounded solution to address water resource limitations and enhance operational efficiency. The proposed system relies on a soil moisture sensor to measure water content and convert it into digital data, which is processed within a microcontroller—specifically the Arduino Uno—within an embedded systems environment. A threshold-based control algorithm, supported by a hysteresis band, is implemented to minimize oscillations in switching decisions. Based on this approach, control signals are generated to operate a 12V DC water pump via a relay module, ensuring a stable real-time response while reducing both energy consumption and computational resource usage. The study is grounded in the quantitative modeling of soil moisture data, including the transformation of sensor readings into percentage values through experimental calibration equations. A calibration table is also developed to establish a relationship between digital sensor outputs and the physical states of the soil. A series of experiments were conducted under varying operating conditions to evaluate system performance in terms of accuracy, response time, stability, and water use efficiency. The results demonstrate a significant improvement compared to conventional time-based irrigation methods, with reduced switching frequency achieved through the implementation of well-defined decision thresholds. The system is characterized by its structural simplicity, scalability, and low cost, offering a practical scientific solution that integrates modeling and control within an embedded systems framework. Furthermore, this work paves the way for future developments, including the integration of communication technologies and the Internet of Things (IoT), as well as the adoption of advanced control algorithms to enhance system accuracy and operational efficiency.