Comparative Analysis of Soil Moisture- and Weather-Based Irrigation Scheduling for Drip-Irrigated Lettuce Using Low-Cost Internet of Things Capacitive Sensors.

Abstract

Efficient irrigation management is crucial for optimizing water use and productivity in agriculture, particularly in water-scarce regions. This study evaluated the effectiveness of soil-based and weather-based irrigation management using a low-cost (DIY) Internet of Things (IoT) capacitive soil moisture sensor on drip-irrigated lettuce. A field experiment was conducted to compare water productivity and water use efficiency between the two management approaches. The soil-based system utilized real-time data from IoT sensors to guide irrigation scheduling, while the weather-based system relied on evapotranspiration data. The IoT-enabled system used 28.8% less water and reduced the pumping hours by 16.2% compared with the conventional weather-based methods. In terms of crop water productivity (CWP), the IoT system reached 16 kg/m³, which was 52.5% higher than the conventional method (10.5 kg/m³). Furthermore, the developed DIY sensor was compared with existing commercial soil moisture sensors, namely, Teros 54 and Drill& Drop. The developed prototype demonstrated reliability and accuracy comparable to other commercial sensors, with an R² = 0.6, validating its utility for enhanced data-driven irrigation, giving its initial low cost (USD 62). These findings highlight the potential of low-cost soil-based IoT systems in enhancing irrigation efficiency and supporting sustainable agriculture, particularly in resource-limited settings.