关键词: 蒸腾耗水, 机器学习, 特征选择, 智慧农业, 水资源管理

Abstract:

[Objective] As the core physiological process determining crop water demand, transpiration profoundly influences irrigation-district water regulation and allocation strategies. During the dry season, water stress is particularly acute in the Yunnan savanna region. For smart, sensor-driven irrigation management, reliable orchard-scale transpiration estimation is essential for real-time irrigation scheduling and water allocation. However, traditional mechanistic models face challenges such as difficulties in parameter acquisition, high sensitivity to specific parameters, and limited applicability in complex terrains. To address these issues in the development of smart irrigation districts, a high-precision citrus transpiration estimation model was constructed with low parameter dependence. [Methods] A citrus orchard with 10-year-old trees in the savanna region was selected as the study site. Three years of stem sap flow observations and synchronous environmental-factor data were collected. Sap flow was accumulated to the daily scale and used to estimate field-scale transpiration. A Random Forest–based wrapper feature selection method was applied to identify an optimal subset of key predictors from 16 original environmental parameters and the day of year (DOY). Specifically, the wrapper method iteratively removed the variable with the lowest feature importance and rebuilt the model, continuing until only one variable remained. During this process, model performance under different numbers of variables was recorded for each iteration, and the best feature subset was determined based on performance. In addition, a heatmap was used to quantify linear correlations among variables to better understand redundant parameters removed by the model. After identifying the optimal feature combination, the built-in feature importance method of Random Forest was used for importance evaluation. Based on these parameters, extreme gradient boosting (XGB), support vector regression (SVR), and random forest (RF) models were established. Particle swarm optimization (PSO), bayesian optimization (BO), and the asynchronous successive halving algorithm (ASHA) were introduced for hyperparameter tuning, resulting in 12 simulation models in total. The dataset was split into 80% for training and 20% for validation. During training, 10-fold cross-validation was adopted, and mean absolute error (MAE) was used as the fitness function. Model performance was comprehensively evaluated using the coefficient of determination (R²), root mean square error (RMSE), MAE, and uncertainty metrics. [Results and Discussions] The RF wrapper feature selection reduced the 16 meteorological input parameters to four: actual vapor pressure (e_a), soil water content (VWC), wind speed at 2 m (u₂), and mean air temperature (T_a). In addition, the DOY was readily obtainable and was identified as the most critical feature, effectively characterizing the unique seasonal climatic variations of the Savanna Valley and citrus phenological characteristics; meanwhile, e_a, VWC, T_a, and u₂ mainly characterize short-term atmospheric evaporative demand and soil moisture constraints. While substantially reducing data requirements, the proposed method still achieved high prediction accuracy. Among all model combinations, XGB-ASHA performed best, reaching R²=0.91 and RMSE=0.19 mm/d for daily-scale transpiration prediction. Hyperparameter optimization improved model robustness, further reducing RMSE and MAE compared with non-optimized baselines. Compared with commonly used mechanistic models in the region, the proposed machine-learning approach does not require complex parameter calibration, is less sensitive to input errors, and shows better application feasibility. Uncertainty evaluation further indicates that this framework can provide not only accurate point predictions but also reliable confidence information for operational decision-making. [Conclusions] The proposed model requires monitoring only four environmental parameters to achieve high-accuracy, operational daily-scale transpiration estimation for citrus orchards in the savanna region. The optimal configuration, XGB-ASHA, is suitable for embedding into smart irrigation systems, supporting water-saving irrigation scheduling and irrigation-district-scale water management. Future work will integrate multi-source data and explore more advanced learning models to further improve transferability and generalization across orchards and years.

Key words: transpiration water consumption, machine learning, feature selection, smart agriculture, water resource management