A NOVEL HYBRID CLUSTERING AND ENSEMBLE LEARNING FRAMEWORK FOR AGRICULTURAL CROP RECOMMENDATION
提出聚类加集成学习的混合框架，用土壤气候数据做作物推荐。
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AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence
构建集成作物推荐、产量预测、肥料建议与多语言问答的AI农业决策Web平台。
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Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation
综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。
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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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A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture
系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。
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Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches
综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。
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AgriSphere: A Smart Agriculture Framework Integrating IoT and Artificial Intelligence for Adaptive Crop Selection
提出IoT与AI融合框架，结合实时土壤数据与天气预报推荐适宜作物。
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IoT-Enabled Evidence-Fusing Graph Neural Network With Attention-Based Feature Engineering For Uncertainty-Aware Crop Recommendation（IJAIML Vol.6 No.9s 1668-1689）
提出IoT-AM-EFGNN框架，结合IoT传感与图神经网络实现高精度作物推荐。
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