Performance of A Soil Moisture-Based Drip Irrigation System Under Contrasting Rainfall Conditions in Tomato Cultivation

Efficient irrigation management is essential to reduce water use in agriculture, particularly under conditions of variable rainfall. The proliferation of open-source hardware and low-cost sensors has enabled the development of automatic irrigation systems based on soil moisture sensing; however, their performance under real rainfall conditions remains insufficiently documented. The objective of this study was to develop and evaluate a soil-moisture-based automatic irrigation system with a Human–Machine Interface (HMI), capable of maintaining a minimum soil moisture threshold while integrating rainfall under field conditions. The system was built using an Arduino microcontroller, soil moisture sensors, a tipping-bucket rain gauge, and a drip irrigation system, and was tested on potted tomato plants (Solanum lycopersicum var. cerasiforme) at two nearby sites with contrasting rainfall conditions. Results showed that the system successfully maintained soil moisture within user-defined thresholds while minimizing irrigation events. Total irrigation water applied during the crop cycle was 12.09 L per plant at Site 1 and 13.67 L per plant at Site 2, indicating that rainfall supplied a substantial portion of crop water requirements. Irrigation frequency was significantly higher during periods of low rainfall (1.93–2.16 irrigations day⁻¹) than during the rainy period (0.51–0.59 irrigations day⁻¹), demonstrating the adaptive response of the system to environmental conditions. These results indicate that soil-moisture-based irrigation systems integrating rainfall can significantly reduce irrigation water use while maintaining adequate soil moisture levels, highlighting their potential for improving water-use efficiency in agricultural production under variable climatic conditions.