An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning models accurately predict daily crop coefficients (Kc) for soybean irrigation management. The Extra Tree model showed the highest accuracy, improving water use efficiency in arid regions.
Area Of Science
- Agricultural Science
- Environmental Science
- Data Science
Background
- Increasing water scarcity and climate variability necessitate precise agricultural irrigation management.
- Accurate estimation of crop coefficients (Kc) is crucial for determining crop water needs, especially in arid and semi-arid regions.
- Conventional Kc estimation methods may not capture local climatic variations.
Purpose Of The Study
- To predict the daily crop coefficient (Kc) for soybean using machine learning models.
- To evaluate the interpretability and physical consistency of these models.
- To provide a robust framework for improving Kc estimation and supporting sustainable irrigation.
Main Methods
- Four machine learning models (Extra Tree, XGBoost, Random Forest, CatBoost) were employed.
- Models were trained on meteorological data from Suhaj Governorate, Egypt (1979-2014).
- Interpretability was assessed using SHapley Additive exPlanations (SHAP), Sobol sensitivity analysis, and Local Interpretable Model-agnostic Explanations (LIME).
Main Results
- The Extra Tree (ET) model achieved the highest accuracy (r=0.96, NSE=0.93, RMSE=0.05, MAE=0.02).
- XGBoost and Random Forest models also demonstrated high performance.
- Antecedent crop coefficient and solar radiation were identified as key influential variables by SHAP and Sobol analyses.
Conclusions
- Interpretable machine learning models enhance the accuracy and reliability of daily Kc estimation.
- The study highlights the importance of aligning model predictions with physical processes for robust agricultural management.
- The proposed framework supports sustainable irrigation practices and climate-resilient agriculture.
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