机器学习,深度学习,合体学习和可解释的人工智能技术的最新趋势,用于在异常气候条件下评估作物产量.
Ji Won Choi1, Mohamad Soleh Hidayat1, Soo Been Cho1
1Department of Biosystems Engineering, College of Agricultural and Life Sciences, Gyeongsang National University, 501, Jinju-daero, Jinju 52858, Republic of Korea.
Plants (Basel, Switzerland)
|September 27, 2025
概括
先进的人工智能 (AI) 方法通过分析环境因素和利用遥感显著改善了作物产量预测 (CYP). 特性选择提高了准确性,为精准农业铺平了道路.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 农作物产量预测 (CYP) 对农业生产力和减轻气候变化影响至关重要.
- 异常的气候,害虫和土壤退化威胁着全球粮食安全.
研究的目的:
- 审查人工智能 (AI) 在作物产量预测中的应用.
- 探索遥感,环境因素和产量减少原因.
主要方法:
- 在CYP中对机器学习 (ML),深度学习 (DL),集体学习和可解释AI (XAI) 的审查.
- 分析遥感 (超光谱和多光谱成像) 和环境数据.
- 对特征选择技术和常见算法 (RF,SVM,ANN,CNN) 的评估.
主要成果:
- 逐步的特征选择比增加特征体积更能提高模型的准确性.
- 随机森林,SVM,ANN和CNN是经常使用的算法.
- 通过无人机进行超光谱和多光谱成像是常见的传感技术.
结论:
- 人工智能,特别是ML和DL,为准确的作物产量预测提供了强大的工具.
- 可解释的人工智能显示出解释复杂CYP模型的前景.
- 了解环境因素和产量减少的原因是推进精准农业和政策的关键.
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