在智能农业中,为优越作物推提供物理意识的集体学习
Hemalatha Gunasekaran1, Krishnamoorthi Ramalakshmi2, Saswati Debnath2
1College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri 516, Oman.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
基于物理的机器学习 (ML) 模型通过整合物理定律来增强作物建议. 这种新的方法显著提高了与传统的ML和集体学习 (EL) 方法相比,精准农业的预测准确性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 农业对国家经济至关重要,需要在作物推系统中取得进展,以提高生产力和资源管理.
- 精准农业的传统机器学习 (ML) 和集体学习 (EL) 模型面临着噪音较大或训练数据有限的局限性,导致预测不准确.
- 将物理定律集成到ML框架中可以确保预测在物理上是合理的,克服数据依赖模型的局限性.
研究的目的:
- 分析和比较ML和EL模型的性能与农业应用的新型物理信息ML模型.
- 在训练过程中引入一个基于物理知识的堆叠ML模型,该模型结合了作物特定的物理定律 (最佳温度和pH).
- 评估模型同时适应数据和遵守物理约束的能力.
主要方法:
- 开发基于物理知识的堆叠ML模型,将最佳作物温度和pH作为物理约束因素纳入其中.
- 训练基于物理学的模型具有双重目标:通过损失函数中的惩罚项来适应训练数据和满足物理定律.
- 对拟议模型与标准ML和优化EL模型进行比较分析.
主要成果:
- 拟议的堆叠物理信息的ML模型实现了最高准确率99.50%.
- 这种性能明显超过了传统的ML和EL模型,即使是那些具有优化的模型.
- 基于物理学的方法确保预测既准确又在物理上可行.
结论:
- 基于物理的ML模型通过整合领域知识,为作物推和精准农业提供了更好的方法.
- 这种基于物理学的新型堆叠模型表现出高精度和可靠性,解决了纯数据驱动方法的局限性.
- 这种方法在提高农业生产率和可持续资源管理方面具有重大潜力.
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