用可解释的集成学习提升作物适种预测，精准识别关键土壤因素。
An Explainable Ensemble Learning Framework for Crop Classification with Optimized Feature Pyramids and Deep Networks
- 融合优化特征金字塔与深度网络，构建可解释的集成模型。
- 在3867条数据上达到98.8%的准确率，显著优于单个模型。
- 适合农业决策者使用，能揭示土壤pH、氮、锌等关键影响因子。
农业正面临气候变化、土壤退化和资源枯竭的挑战，亟需基于数据驱动的作物分类与推荐方案。本文提出一种可解释的集成学习框架，融合优化的特征金字塔、深度网络、自注意力机制与残差网络，基于土壤特性（如pH、氮、钾）和气候条件（如温度、降雨量）提升作物适宜性预测。利用埃塞俄比亚农业转型署与NASA提供的3867条样本、29个特征的数据集，通过标签编码、四分位距剔除异常值、StandardScaler归一化及SMOTE平衡类别。对比了逻辑回归、K近邻、支持向量机、决策树、随机森林、梯度提升及新提出的相对误差支持向量机，并采用网格搜索与交叉验证进行超参数调优。所提“最终集成”元集成设计表现最优，准确率、精确率、召回率与F1分数均达98.80%，远超K近邻（95.56%）。结合SHAP与置换重要性等可解释AI方法，揭示土壤pH、氮、锌为关键影响因子。该框架弥合复杂机器学习模型与可操作农业决策之间的鸿沟，推动人工智能推荐的可持续性与可信度。
原文摘要 · Abstract (English)
Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble learning paradigm that fuses optimized feature pyramids, deep networks, self-attention mechanisms, and residual networks for bolstering crop suitability predictions based on soil characteristics (e.g., pH, nitrogen, potassium) and climatic conditions (e.g., temperature, rainfall). With a dataset comprising 3,867 instances and 29 features from the Ethiopian Agricultural Transformation Agency and NASA, the paradigm leverages preprocessing methods such as label encoding, outlier removal using IQR, normalization through StandardScaler, and SMOTE for balancing classes. A range of machine learning models such as Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting, and a new Relative Error Support Vector Machine are compared, with hyperparameter tuning through Grid Search and cross-validation. The suggested "Final Ensemble" meta-ensemble design outperforms with 98.80% accuracy, precision, recall, and F1-score, compared to individual models such as K-Nearest Neighbors (95.56% accuracy). Explainable AI methods, such as SHAP and permutation importance, offer actionable insights, highlighting critical features such as soil pH, nitrogen, and zinc. The paradigm addresses the gap between intricate ML models and actionable agricultural decision-making, fostering sustainability and trust in AI-powered recommendations
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