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.

Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R²(coefficient of determination) = 0.66, VIs: R² = 0.61), PNA (CIs: R² = 0.68, VIs: R² = 0.64), and NNI (CIs: R² = 0.64, VIs: R² = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models’ performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models’ performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring.

Water application uniformity is an important performance parameter when designing and operating an irrigation system. Performance tests of a center pivot irrigation system equipped with fixed and rotated spray plate sprinklers (FSPS and RSPS, respectively) were conducted at five travel speeds. The effects of travel speed, collector size, and setting height on water application uniformity were evaluated using Heermann and Hein’s coefficient of uniformity (CU_H). The CU_H was 12.7% higher for the RSPS than the FSPS and decreased as the travel speed increased. Collector size and setting height affected CU_H, and CU_H was higher when the collector had a large opening cross-section compared to the collector with a small opening cross-section. CU_H was higher when the collector with a low setting height compared to when it a high setting height for the FSPS. However, collector setting height had no effect on CU_H for the RSPS. The weighted average water application depth (D_w) decreased as the travel speed increased. Collector size had no significant effect on D_w, but D_w with a low collector setting height was larger than the values with a high collector setting height. The water application rate increased as distance from the pivot point increased and was higher for the FSPS than the RSPS. The results will improve the selection of travel speed and collector when the water application uniformity of a center pivot irrigation system is evaluated.

Droplet impact angle and shear stress are important indicators of surface runoff under sprinkler irrigation, and determining the distribution characteristics of these two indicators on sloping land is of great significance for preventing soil surface erosion. Therefore, three slopes (0, 10%, and 20%) and two directions (uphill and downhill) under a Rainbird LF1200 rotary sprinkler were considered in this study. The distribution of droplet impact angles and shear stresses along the radial direction were investigated under various working conditions. The correlations among the droplet impact angle, shear stress, and distance from the sprinkler were also analyzed. These results indicated that the closer to the sprinkler, the larger the droplet impact angle and the smaller the shear stress, and the two indicators gradually decreased and increased with the increase of distance from the sprinkler, respectively. Accordingly, there was a very high potential for soil surface runoff at the spray jet end. It was also observed that the uphill direction generally had a greater impact angle and less shear stress than flat land, while the downhill direction had exactly the opposite result. However, regardless of the direction, an increase in the slope could intensify its effect on the droplet shear stress and impact angle. Therefore, there is an urgent need to focus on the occurrence of surface runoff in soils with larger slopes. In addition, two radial droplet shear stress distribution models were developed, and it was verified that Model 2 had higher accuracy (MAE = 176.6 N m⁻², MBE = 32.8 N m⁻², and NRMSE = 14.4%) and could be used to predict the average droplet shear stresses at different slopes, directions, and distances from the sprinkler. This study contributes to the soil erosion prevention and the sprinkler irrigation system optimization on sloping land.

Subsurface drip irrigation is an advanced technique that significantly enhances agricultural water efficiency and conserves irrigation resources. The Heilonggang region is highly representative of the maize–wheat rotation system in China. This region was selected for conducting operations and maintenance experiments on subsurface drip irrigation systems. The primary objective of this study was to determine the most suitable type of drip tape for application in the North China Plain and to identify specific maintenance measures necessary to ensure the long-term functionality of subsurface drip irrigation systems. The experiment was conducted in Jing County, Hengshui City. Anti-blocking drip tape (Netafim Co., Ltd. Beijing, China) with automatic functionality was evenly laid in the test area. The experimental area was divided into six rotational irrigation groups. The key parameter examined in this single-factor experiment was the drip tape wall thickness, with values of 0.2, 0.225, 0.25, 0.28, 0.31, and 0.38 mm. Drip tape treatments were randomly sampled in rotational irrigation groups, and there were three repeat plots in each treatment. Each replicate plot contained ten drip irrigation belts spaced 60 cm apart, with an interval of more than 2 m between adjacent plots. The subsurface drip irrigation system was installed in October 2023. Prior to irrigation, drip tape troubleshooting was conducted and recorded manually on 12 March 2024 (wheat regreening stage) and 29 June 2024 (maize-sowing stage). The experimental findings indicated that the primary factor influencing the stability of the irrigation system was the wall thickness of the drip tapes, while other system components operated efficiently. A significant correlation was observed between the wall thickness of the drip tape and the number of water leakage points (p < 0.05), with an absolute correlation coefficient exceeding 0.9. The number of leakage points in drip tapes with wall thicknesses of 0.2–0.28 mm (267 instances) was significantly higher than those with wall thicknesses of 0.31–0.38 mm (29 instances), primarily due to damage caused by mole crickets and wireworms. Following the injection of 40% phoxim, 2.5% lambda-cyhalothrin, and 70% imidacloprid insecticides (at a cost of 16.7 USD·ha⁻¹) into the subsurface drip irrigation system, the insect pests were nearly eradicated within one month. A cumulative cost evaluation over a 6–10-year period recommended the use of drip tapes with a wall thickness of 0.31 mm and the application of insecticides every 1–2 months to maintain optimal system performance in this region. These measures can effectively support the stable operation of this irrigation technique at a relatively low cost.

Knowledge of the response of growth parameters of almond (Badam) trees to different water supply conditions is important for Badam production. To identify growth parameters that are sensitive to water deficit in almond trees, field experiments were conducted during a median water year to monitor the major growth characteristics of almond trees under different irrigation conditions in Shache County, Xinjiang, China. A field (in-situ) monitoring system was also constructed using various sensors for the continuous and non-destructive monitoring of the growth parameters, such as soil water in the root zone, canopy temperature depression, trunk diameter, and fruit diameter of almond trees. The results confirmed the reliability of the monitoring system. Both canopy temperature depression and the diameter shrinkage of the trunk and fruit were significantly negatively correlated (r values ranging from −0.996 to −0.823) with the irrigation water quantity. This correlation was observed from the young fruit stage to the maturation stage of almond trees, under irrigation conditions representing 50–100% of crop evapotranspiration (ETc). These parameters were sensitive (|r| ≥ 0.778) to the water deficit status of almond trees from 14:00 to 18:00 in sunny weather. These results can provide both technical and theoretical support for real-time non-destructive assessment of the water deficit status of almond trees.