The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (g_s) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, g_s was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that g_s at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the g_s value and SWSI value was high (R² > 0.79). Therefore, the g_s value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R² increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R²: 0.69–0.87, RMSE: 0.04–0.08 mol m⁻² s⁻¹; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m⁻² s⁻¹) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation.

This study investigates the effects of nitrogen application and sprinkler irrigation on winter wheat growth, water use efficiency (WUE), and yield formation under dry-hot wind stress. The primary aim was to understand how nitrogen levels influence canopy structure, soil water–nitrogen coupling, and yield components under varying irrigation conditions. Field experiments were conducted with different nitrogen rates (N1, N2, N3, N4, N5) and sprinkler irrigation under heat stress. Plant height, leaf area index (LAI), canopy interception, and stemflow were measured, along with soil moisture and nitrogen content in the root zone. Results indicate that moderate nitrogen application (212 kg N ha⁻²) optimized yield and WUE, with a significant enhancement in canopy structure and water interception. High nitrogen levels resulted in increased water consumption but decreased nitrogen use efficiency (NUE), while lower nitrogen treatments showed reduced yield stability under heat stress. The findings suggest that balanced nitrogen management, in combination with timely irrigation, is essential for improving winter wheat productivity under climate stress. This study highlights the importance of optimizing water and nitrogen inputs to achieve sustainable wheat production in regions facing increasing climate variability.