Exploratory Explainable Machine Learning for Crop-Yield Interpretation: A Comparative Analysis of XGBoost, LightGBM, and CatBoost
比较XGBoost、LightGBM和CatBoost在作物产量可解释建模中的表现，强调评估协议与数
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没有这个标签，内容接近Design and Implementation of an Ensemble Learning Based Decision Support Model for Crop Selection in Precision Agriculture
提出SCR-XGB五层框架，用梯度提升树集成从土壤和气候参数推荐作物。
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Image-Based Soil Nutrient Prediction Using Machine Learning and Deep Learning Models
用土壤图像结合机器学习与深度学习模型预测氮磷钾含量。
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Unraveling nonlinear ecosystem water use efficiency dynamics and environmental drivers across Iran using remote sensing and XGBoost-SHAP
用遥感与XGBoost-SHAP解析伊朗2001-2025年生态系统水分利用效率的非线性动态及驱动因
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Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model
基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。
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Risk-based prioritization of official animal welfare inspections using machine learning
用机器学习基于常规行政与兽医数据，对奥地利农场动物福利违规风险进行优先级排序。
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Development of an Explainable GeoAI Wildfire Risk Analysis Model Using Multi-Source Geospatial Data
构建可解释GeoAI框架，融合多源地理数据评估土耳其野火风险并制图。
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Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models
评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。
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