Publication Details
Abstract
This study presents the design, development, and performance evaluation of a low-cost capacitive moisture sensor integrated with an automated home irrigation system, aimed at improving water-use efficiency in small domestic gardens (2 × 3 m²). Field experiments were conducted using a three-replicate randomized complete block design (RCBD) to evaluate the effects of three main factors: soil texture (sandy, loamy, clay), soil temperature (15 °C, 25 °C, 35 °C), and installation depth (5 cm, 10 cm, 15 cm) on six key performance parameters of the sensor—accuracy, stability, response time, thermal sensitivity, salinity sensitivity, and energy consumption. The results indicated that soil texture had the most significant influence on accuracy, with loamy soil showing the lowest value (2.1%) compared to sandy (4.8%) and clay (3.5%) soils. Stability followed a similar trend, being highest in loamy soil (0.4% SD) and lowest in sandy soil (0.9% SD). Soil temperature strongly affected thermal sensitivity, which increased from 0.05% at 15 °C to 0.12% at 35 °C, and reduced the response time (T₉₀) from 27 seconds at low temperature to 18 seconds at high temperature. Depth of installation is associated with the sensitivity of salinity measurement, with decreased salinity measurement sensitivity (1.8% per S/m²) and energy consumption (42 mWh/day) for surface mounted probes (5 cm) versus for deeper probes (0.9% per S/m² and 36 mWh/day, respectively). These findings highlight the critical importance of considering environmental and operational factors—such as soil texture compatibility, thermal compensation, and optimal probe depth—when designing and applying low-cost moisture sensors for home irrigation. The developed system provides a scientific foundation for creating affordable, accurate, and energy-efficient smart irrigation solutions tailored to domestic green spaces.