从数据到决策:使用可解释的人工智能来预测主要生产国的大豆产量
Xiangyi Wang1,2, Yingbin He3,4, Huicong Chen1,2
1State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China.
Scientific reports
|January 13, 2026
概括
像科尔莫戈罗夫-阿诺德网络 (KAN) 这样的可解释人工智能 (XAI) 模型提供了与深度学习 (DL) 模型可比的准确作物产量预测. KAN提高了透明度,这对于农业决策和全球粮食安全至关重要.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 可解释的人工智能
背景情况:
- 准确的作物产量估计对于全球粮食安全和贸易至关重要.
- 深度学习 (DL) 模型在预测方面表现出色,但缺乏透明度,阻碍了信任.
- 可解释AI (XAI) 旨在平衡模型解释性与预测准确性.
研究的目的:
- 提出和评估XAI-Crop框架,以实现透明的作物产量估计.
- 为了比较Kolmogorov-Arnold网络 (KAN) 与多层感知器 (MLP) 和随机森林 (RF) 模型的性能.
- 通过使用多来源数据,评估收益率驱动因素的区域变异性.
主要方法:
- 开发了使用多源数据的XAI-Crop框架.
- 使用主要大豆生产国进行了比较实验.
- 评估了科尔莫戈罗夫-阿诺德网络 (KAN),多层感知器 (MLP) 和随机森林 (RF) 模型.
主要成果:
- 在小样本环境中,KAN表现出与MLP和RF相比的预测准确性和概括性.
- 与MLP和RF相比,KAN提供了更好的解释性.
- 太阳引起的叶绿素光 (SIF) 被确定为所有地区的一致敏感预测因素,突出了产量驱动因素的区域变化.
结论:
- 以KAN为例的XAI方法可以有效地弥合模型准确性和可解释性之间的差距.
- XAI-Crop框架显示了整合到农业决策支持系统的可行性.
- 通过提高产量估计的透明度,研究结果有助于可持续发展的农业发展.
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