Film hole irrigation is a relatively low cost and high efficiency irrigation method, which can significantly improve the efficiency of agricultural water use. In order to establish the quantitative model of film hole irrigation between cumulative infiltration and the wetting body and the irrigation volume model of crops, the infiltration process and wetting body characteristics of four different soils (Xi’an silt loam, silt, silt loam and loam) were studied in laboratory experiments and numerical HYDRUS simulation experiments. The relationship between cumulative infiltration and wetting body radius was established using a mathematical method, and a crop irrigation volume model was proposed based on the root distribution and the required water content of different crops. The experimental results showed that the shape of the wetting body of film hole irrigation is approximately half of the rotating ellipsoid, and the curve shape of the wetting front can be expressed using an elliptic equation. From the center of the film hole to the surface of the wetting front, the soil water content of the wetting body gradually decreases, and the change rate of water content gradually increases, reaching its maximum value near the wetting front. Furthermore, the distribution of water content in the wetting body can be accurately expressed using an elliptic curve equation. The cumulative infiltration of film hole irrigation is proportional to the third power of the equivalent radius of the wetting body, and the equivalent radius is equal to the geometric mean of the horizontal and vertical migration distances of the wetting front. In addition, based on the distribution of crop roots and the demand of crop roots on soil water content, the irrigation model of crops was established. This study provides a theoretical basis for the calculation of the irrigation volume for film hole irrigation under the condition of experiment, and has a guiding significance for the field experiment and application of film hole irrigation in different crops in future.

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions.

Evaptotranspiration (ET_c) is a crucial link in the farmland water cycle process. To accurately obtain the citrus ET_c in different slope positions, the METRIC, RSEB, and FAO Penman–Monteith (P-M) models were constructed based on unmanned aerial vehicle (UAV) multispectral images to invert the ET_c values. The ET_c of citrus calculated by the P-M model was used as a reference standard, and the accuracy of the ET_c inversion was evaluated by the METRIC model and the RSEB model. The results showed that the R², RMSE, and SE of the METRIC model and the RSEB model were 0.396 and 0.486, 4.940 and 3.010, and 4.570 and 2.090, respectively, indicating a higher accuracy of the RSEB model for inverting the ET_c values. Furthermore, the accuracy of the RSEB model could be improved by introducing the optimal correction coefficient (after correction: RMSE = 1.470, SE = 0.003). Based on the modified RSEB model, the ET_c values of the citrus in different slope positions were obtained. We also found that the middle slope ET_c > the top slope ET_c > the bottom slope ET_c, indicating that the slope position indeed affected the citrus ET_c. This research provides a favorable framework for the ET_c inversion, and the results are of theoretical and practical importance to realize crop water conservation.

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management.