Moisture and Salinity Distribution under Artificial Intelligence Based Systems and Traditional Farming Practices, and Their Influence on Yellow Corn Yield

Abstract

This study was conducted at the research station of, College of Agriculture, University of Diyala, during the summer growing season of 2025. It aimed to optimize water consumption for maize by comparing a traditional irrigation system with a smart irrigation system supported by artificial intelligence technologies. It also evaluated the impact of two soil moisture levels (50% and 75% of field capacity) on water consumption, water use efficiency, crop yield. The smart irrigation system relied on soil moisture sensors (TDT) for automated irrigation control, while the traditional irrigation system was operated manually. The entire irrigation system was powered by solar energy. The moisture distribution analysis outcomes under the drip irrigation system showed a clear spatial gradient in soil moisture content. The highest moisture ratios were concentrated near by the emitters and within the surface layers, then gradually decreased with horizontal distance from the water source and with increasing depth. By optimizing irrigation timing and volume based on sensor-driven data, the smart irrigation system ensured more consistent moisture distribution across the root zone compared to traditional practices. Regarding salinity distribution. The lowest salinity levels were recorded near the drip emitters, attributed to the continuous leaching of salts by irrigation water. Conversely, salt concentrations gradually increased toward the periphery of the wetting front and in areas distant from the water source, driven by water driven salt transport and subsequent accumulation at the wetting front boundaries. Furthermore, precise irrigation management through use the smart system helped reduce salt accumulation within the root zone compared to the traditional system.