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.

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.

Mobile drip irrigation (MDI) systems integrate the technological advantages of center-pivot irrigation (CPI) systems and drip irrigation systems, boasting a high water-saving potential. To further enhance water use efficiency in alfalfa production in northern China, this preliminary study verified the accuracy of the HYDRUS-2D soil water movement numerical model through field experiments. Using the numerical model, four drip-line installation distances (60, 75, 90, and 105 cm), three deficit irrigation thresholds (45–50% FC, 55–60% FC, and 65–70% FC), and four irrigation depths (70% W, 85% W, 100% W, and 115% W) were set to simulate root water uptake, soil surface evaporation, total irrigation amount, and deep percolation during the entire growth cycle of alfalfa, respectively. Objective functions were constructed according to the simulation results, and the NSGA-II algorithm was used for multi-objective optimization of the deficit irrigation schedule. The preliminary results indicated that HYDRUS-2D can accurately simulate soil water movement under MDI systems, as the RMSE values of soil water content at all measured depths were less than 0.021 cm³/cm³, with the NRMSE values being below 23.3%, and the MAE values below 0.014 cm³/cm³. Increasing the deficit irrigation threshold from F1 to F3 enhanced root water uptake by 12.24–15.34% but simultaneously increased the total irrigation amount, soil surface evaporation (by up to 29.58%), and the risk of deep percolation; similar trends were observed with increasing irrigation depth. The drip-line installation distance had no significant impact on irrigation performance. The NSGA-II multi-objective optimization algorithm was used to obtain Pareto-optimal solutions that balance conflicting objectives. For this case study, a drip-line installation distance of 105 cm, a deficit irrigation threshold of 50–55% FC, and an irrigation depth of 112% W were recommended to achieve balance among the various optimization objectives. This study provides a preliminary framework for optimizing MDI systems and irrigation strategies. However, since a deeper root distribution (>80 cm) was not investigated in this study, future research incorporating deeper root zones is required for developing more comprehensive irrigation scheduling suitable for typical alfalfa cultivation scenarios.