Drip irrigation pressure is considered a key parameter for controlling and designing the drip irrigation system in sandy soils. Understanding soil water content (SWC) movements under varying pressures can enhance water use efficiency and support sustainable irrigation strategies for crops in arid regions. The objectives of this study were to investigate the effects of irrigation pressure on the spatial, vertical, and temporal variability of SWC in sandy loam soil using surface drip irrigation. Experiments were carried out in a soil bin located in a greenhouse. SWC sensors were placed at depths 10, 20, 30, 40, and 50 cm to monitor SWC variability under low, medium, and high drip irrigation pressures (25, 50, and 75 kPa) at a constant emitter flow rate of 3 L/h. A pressure controller was used to regulate drip irrigation pressure, while microcontrollers communicated with SWC sensors, collected experimental data, and automatically recorded the outputs. At low irrigation pressure, water content began to increase at 0.53 h and saturated at 3.5 h, with both values being significantly lower at medium and high pressures. The results indicated that lower pressures led to significant variability in water movement at shallow depths (10 to 30 cm), becoming uniform at deeper layers but requiring longer irrigation times. Competitively higher pressures showed uniform water distribution and retention statistically throughout the soil profiles with shorter irrigation times. The variation in water distribution resulting in non-uniform coverage across the irrigated area demonstrates how pressure changes affect the flow rate of the emitter. The results provide information maps with soil water data that can be adjusted with irrigation pressure to maximize water use efficiency in sandy loam soils, aiding farmers in better irrigation scheduling for different crops using surface drip irrigation techniques in arid environments.

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature and relative humidity at different layers. A glass greenhouse and an arched-frame double-layer plastic greenhouse were monitored during summer and winter, respectively. A wireless sensor network was deployed at multiple spatial positions and vertical layers, and layer-specific artificial neural network (ANN) models were developed to predict indoor temperature and relative humidity at the top, middle, and bottom layers. The measured results revealed clear temperature and humidity stratification, with the top layer generally showing a higher temperature and lower humidity than the middle and bottom layers. In summer, temperatures reached 36.4 °C, while relative humidity ranged from 55% to 92%, while in winter, temperature varied from 3.4 °C to 35.0 °C and relative humidity ranged from 73% to 91%. Spatial contour mapping showed clear microclimatic gradients, and ANOVA with Tukey’s HSD tests confirmed significant differences among sensor locations (p < 0.05). The ANN models predicted indoor temperature with high accuracy, with R² values generally above 0.95, while humidity prediction showed larger errors.