wcm.03.2025.559.567

In regions of Uzbekistan where water resources for irrigation are limited, drip irrigation optimization is of great importance. This study evaluated the effectiveness of the AdaptiveDrip-Uz system. This system includes HYDRUS 2D/3D modeling, artificial intelligence (AI) module, real-time IoT sensors, and GIS-based monitoring components. The model, built on 30 days of field data, automatically controls the irrigation regime based on humidity, temperature, evapotranspiration, and salinity. Of the various AI models, ANN (Artificial Neural Networks) showed the highest accuracy (R2 = 0.951), resulting in a 27% reduction in water consumption and a 24% increase in yield. Moisture and salinity contours, sensor analysis, and a 3D visual interface created through HYDRUS confirmed the high efficiency and flexibility of the system. A SWOT analysis of the system was also conducted, identifying its strengths and weaknesses, and evaluating it as a practical and sustainable innovative solution in the agroecosystems of Uzbekistan. The AdaptiveDrip-Uz model serves as a practical example of a real-time digital agriculture approach adapted to climate change.