下一代农业:整合AI和XAI用于精确的作物产量预测
R N V Jagan Mohan1, Pravallika Sree Rayanoothala2, R Praneetha Sree3
1Department of Computer Science and Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram, India.
Frontiers in plant science
|January 23, 2025
概括
人工智能 (AI) 和可解释AI (XAI) 准确预测气候变化下的作物产量. 温度是关键,人工智能为精准农业和气候适应战略提供了洞察力.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 气候变化通过降水变化和极端天气事件影响全球粮食安全,影响作物产量和农民经济.
- 了解气候和农业学因素之间的复杂相互作用对于制定有效的适应战略至关重要.
研究的目的:
- 使用AI和XAI预测作物产量并评估气候变化对农业的影响.
- 为农业决策和政策制定提供透明的框架.
主要方法:
- 探索性数据分析 (EDA) 以确定影响作物产量的关键气候和农学因素.
- 应用先进的回归模型 (决策树,随机森林,LightGBM) 进行预测性绩效评估.
- 利用可解释的人工智能 (XAI) 技术,包括SHAP和LIME,用于模型解释性.
主要成果:
- 温度被确定为影响作物产量的最关键因素,降雨量和宏观营养素相互作用显著.
- 通过回归模型实现的高预测精度 (R2=0.92,MSE=0.02,MAE=0.015).
- XAI技术为特征的重要性提供了可操作的见解,增强了对模型预测的理解.
结论:
- 人工智能和XAI为减轻气候变化对农业的影响提供了一个强大而透明的框架.
- 该研究的结果支持精准农业和气候政策制定中的可扩展应用.
- 人工智能模型的增强可解释性有助于明智的农业决策和适应规划.
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