The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R² of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.

Water conservation in agriculture is one of the key measures for alleviating the imbalance between water supply and demand, and a comprehensive assessment of the economic effects of agricultural water conservation is crucial for advancing the implementation of water conservation policies. Based on a traditional CGE model, this study incorporates “water resources” as an intermediate input and couples it with a mathematical model of water conservation costs to accurately reflect the relationship between the physical quantity of water resources and their economic value. The study establishes eight water conservation scenarios to simulate the impact of varying levels of water conservation on macroeconomic aggregates, water savings, and water conservation costs, and analyzes the marginal costs and marginal benefits under different conservation intensities. The results indicate that agricultural water saving can significantly improve economic water-use efficiency and drive macroeconomic growth through industrial linkage effects; nevertheless, the scale of reallocation of water saved from agriculture to non-agricultural sectors is limited. As water-saving intensity increases, the rate of change in economic output of each sector gradually rises and then stabilizes during 2022–2030. Among all sectors, the secondary sector exhibits the largest output change rate, ranging from 0.7% to 3.0%, while the tertiary sector shows the smallest change rate, varying from 0.4% to 1.7%. Under scenarios J1–J7, the marginal cost of water saving rises from 19.2 m³ per yuan to 52.1 m³ per yuan, and the marginal output decreases from 81.1 m³ per yuan to 6.8 m³ per yuan. The pattern of marginal cost–benefit follows general economic principles. The economically reasonable water-saving threshold occurs where marginal cost equals marginal benefit, corresponding to a water-saving volume of 8.2 × 10⁸ m³ and agricultural water withdrawal of 43.9 × 10⁸ m³. The research findings can serve as a reference for high-quality water-saving agricultural development in irrigation districts of arid and semi-arid regions.

Reliable quantification of forest aboveground biomass (AGB) and growing stock volume (GSV) underpins carbon stock assessment and ecosystem monitoring. However, the complex mountainous terrain makes it difficult to estimate forest parameters, and research on the simultaneous estimation of forest AGB and GSV remains limited. To this end, this study proposed an eco-constrained explainable multi-task deep learning framework (Eco-AMT-MLP) that integrated Sentinel-2 data obtained from the Google Earth Engine platform with topographic, soil, and climatic factors to jointly estimate forest AGB and GSV while incorporating an ecological constraint linking the two prediction tasks in the complex mountainous forests of northwestern Yunnan Province. The model’s performance was evaluated against Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and K-Nearest Neighbor Regression (KNN), and ablation experiments were conducted to assess the contribution of the ecological constraint module. The Eco-AMT-MLP model demonstrated the best overall predictive performance among the compared models, achieving an R² of 0.64 ± 0.08 and an RMSE of 132.99 ± 19.96 m³ ha⁻¹ for GSV prediction, and an R² of 0.58 ± 0.07 and an RMSE of 87.37 ± 13.10 Mg ha⁻¹ for AGB prediction. Compared with the MLP model, Eco-AMT-MLP increased the R² values for GSV and AGB predictions by 0.04 and 0.11, respectively, while reducing the corresponding RMSE values by 7.46 m³ ha⁻¹ and 10.12 Mg ha⁻¹, respectively. However, the model shows only limited improvement in AGB estimation accuracy, indicating that its enhancement of AGB estimation is less pronounced than its improvement of GSV estimation. SHAP analysis further identified environmental covariates and Sentinel-2 spectral features as important predictors of forest AGB and GSV. By integrating joint estimation of forest AGB and GSV with ecological constraints and interpretable predictor analysis, this framework provides a methodological reference for regional forest carbon-stock assessment, forest resource inventories, and ecosystem monitoring in complex mountainous regions.

Monitoring tree growth helps operators better understand the growth mechanism of trees and the health status of trees and to formulate more effective management measures. Computer vision technology can quickly restore the three-dimensional geometric structure of trees from two-dimensional images of trees, playing a huge role in planning and managing tree growth. This study used binocular reconstruction technology to measure the height, canopy width, and ground diameter of Castanopsis hystrix and compared the growth differences under different nitrogen levels. In this research, we proposed a wavelet exponential decay thresholding method for image denoising. At the same time, based on the traditional semi-global matching (SGM) algorithm, a cost search direction is added, and a multi-line scanning semi-global matching (MLC-SGM) algorithm for stereo matching is proposed. The results show that the wavelet exponential attenuation threshold method can effectively remove random noise in red cone images, and the denoising effect is better than the traditional hard-threshold and soft-threshold denoising methods. The disparity images produced by the MLC-SGM algorithm have better disparity continuity and noise suppression than those produced by the SGM algorithm, with more minor measurement errors for C. hystrix growth factors. Medium nitrogen fertilization significantly promotes the height, canopy width, and ground diameter growth of C. hystrix. However, excessive fertilization can diminish this effect. Compared to tree height, excessive fertilization has a more pronounced impact on canopy width and ground diameter growth.