使用SAM-L预测植物压力:基于土壤水分和叶绿素分析的XAI的新型自我适应的元学习器
Tawfeeq Alsanoosy1,2, Javaid Ahmad Malik3
1Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, 42353, Saudi Arabia. tsanoosy@taibahu.edu.sa.
Scientific reports
|November 27, 2025
概括
这项研究引入了一个新的AI框架,使用稀疏增材模型与学习 (SAM-L) 和可解释AI (XAI) 来准确预测土壤水分和叶绿素的植物压力. 该模型达到89.2%的准确性,有助于精准农业和可持续的用水.
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:
- 植物压力对作物产量和可持续性产生重大影响.
- 准确,实时的植物压力预测对于优化用水和作物健康至关重要.
- 现有的机器学习模型在动态农业环境中难以适应性,可扩展性和可解释性.
研究的目的:
- 开发一种新的框架,将Sparse添加模型与学习 (SAM-L) 和可解释的人工智能 (XAI) 集成在一起,用于植物压力预测.
- 用土壤水分和叶绿素含量来提高植物压力预测模型的准确性,适应性和解释性.
- 为农民和精准农业的利益相关者提供透明的决策工具.
主要方法:
- 使用具有学习 (SAM-L) 的稀疏增量模型进行特征选择和可解释的建模.
- 整合可解释的人工智能 (XAI) 确保透明的决策过程.
- 采用三层长短期记忆 (LSTM) 网络来处理顺序数据.
主要成果:
- 在多类工厂应力分类任务中获得了89.2%的整体准确性.
- 在压力类别 (健康,中等,高) 中获得了0.88的宏观F1得分和0.88的宏观回忆.
- 在使用土壤水分和叶绿素数据预测植物压力水平方面表现强.
结论:
- 拟议的SAM-L和XAI框架为工厂压力预测提供了灵活,透明和准确的解决方案.
- 这种方法提高了预测准确度,通过减少水浪费来支持可持续农业.
- 提高作物弹性和优化资源管理是精准农业的关键好处.
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