Comparative Analysis of Soil Moisture- and Weather-Based Irrigation Scheduling for Drip-Irrigated Lettuce Using Low-Cost Internet of Things Capacitive Sensors
Ahmed A. Abdelmoneim, Christa M. Al Kalaany, Giovana Dragonetti, Bilal Derardja, Roula Khadra
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.