一个可解释的基于人工智能的混合机器学习模型,用于解释性和增强的作物产量预测.
Anuradha Yenkikar1,2, Ved Prakash Mishra1, Manish Bali1
1School of Engineering, Amity University Dubai Campus, Dubai, 25314, UAE.
MethodsX
|July 4, 2025
概括
这项研究介绍了一种混合AI模型与可解释的人工智能 (XAI) 进行准确的农作物产量预测在印度. 提高透明度有助于农民和政策制定者为可持续农业做出明智的决策.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 农业对印度的经济和粮食安全至关重要,需要准确的作物产量预测.
- 机器学习 (ML) 模型提高了收益预测的准确性,但往往缺乏解释性,阻碍了利益相关者的采用.
- 可解释的人工智能 (XAI) 为机器学习模型提供了透明度,促进了信任和知情决策.
研究的目的:
- 开发和验证与XAI技术集成的混合AI模型,以提高作物产量预测.
- 为决策者和农民提高人工智能驱动的农业预测的可解释性.
- 通过一个用户友好的界面,为可持续农业实践提供可操作的见解.
主要方法:
- 实现了一个混合AI模型,结合了随机森林 (RF),长短期内存 (LSTM) 和XGBoost算法.
- 可解释的人工智能 (XAI) 方法,包括SHAP,LIME和反事实分析,被整合为模型的可解释性.
- 利用了来自印度的246,000多份农业记录的综合数据集,涵盖多年,州,作物,季节和气候因素.
主要成果:
- 混合人工智能模型实现了高预测准确性,R2值为0.9827的整体作物产量和0.9721的米产量.
- 整合XAI技术成功提高了预测模型的透明度,揭示了细微的特征相互作用.
- 开发的"E-Kisan"网络界面提供了从模型的预测中获得的可操作的见解.
结论:
- 这项研究证明了将XAI与混合AI模型集成在一起的有效性,以准确和可解释的作物产量预测.
- 这些发现支持采用透明的人工智能工具来改善印度的农业规划和可持续性.
- "E-Kisan"平台为向农业利益相关者传播人工智能驱动的见解提供了一个实际的解决方案.
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