Machine Learning Classification Techniques for Smart Agriculture: A Review of Disease Detection and Crop Recommendation (机器学习分类技术在智慧农业中的应用综述:病害检测与作物推荐)
综述性论文，系统梳理机器学习在农业病害检测与作物推荐中的应用，对智慧农业技术发展有参考价值。
Frontiers in Artificial Intelligence 2026年8月31日综述。研究对2023年1月至2026年1月Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中的文章进行系统检索,使用IoT and machine learning and agriculture、crop recommendation、plant disease detection、precision farming等关键词组合,30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准。结果表明,随机森林( Random Forest)、支持向量机(SVM)和决策树等集成方法与传统机器学习方法在作物产量预测中准确率达到99.9%;CNN和ResNet深度学习模型病害检测准确率超过98%;GRU和LSTM时间序列模型天气条件预测准确率范围94-98%。IoT系统在精准农业应用方面显著改善资源利用,实现60-96%的节水、25%的增产和36-38%的除草剂减量。
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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Volume 9 - 2026 | doi: 10.3389/frai.2026.1878006
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Frontiers in Artificial Intelligence
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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Machine Learning Classification Techniques for Smart Agriculture: A Review of Disease Detection and Crop Recommendation
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VIT University Chennai, Chennai, India
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