CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS
基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。
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From patterns to prediction: detecting crop sequence and interpreting crop selection with explainable AI at field level in Denmark
用丹麦十年田块数据识别轮作模式并预测年度作物选择。
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KRISHI.AI: an explainable machine learning framework for data-driven crop recommendation
提出可解释AI框架KRISHI.AI，基于土壤和气候数据推荐作物，并解释推荐原因。
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Ai Based Smart Crop Recommendation and Yield Prediction Using Ml and Weather Analytics
提出AI框架，结合机器学习与天气分析，推荐作物并预测产量。
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