影子色预测在18 GHz通过物理指导学习在植物走廊的影子色预测
Jorge Celades-Martínez1, Melissa E Diago-Mosquera2, Alvaro Peña3
1Doctorado en Industria Inteligente, Pontificia Universidad Católica de Valparaíso, Valparaíso, 2362804, Chile. jorge.celades.m@mail.pucv.cl.
Scientific reports
|January 21, 2026
概括
精准农业的准确无线模型需要考虑果园的几何形状. 混合物理机器学习方法显著改善了在不同走廊宽度和发射器高度的无线电传播预测.
科学领域:
- 农业工程 农业工程
- 无线通信无线通信
- 机器学习 机器学习
背景情况:
- 精准农业依赖于强大的无线连接.
- 现有的无线电传播模型与可变果园几何形状作斗争.
- 准确的模型对于可靠的传感器网络和自动化至关重要.
研究的目的:
- 为精准农业开发和验证一个改进的无线电传播模型.
- 评估果园走廊几何形状对18 GHz无线信号传播的影响.
- 将混合物理-ML模型与传统模型进行比较.
主要方法:
- 在果果园进行了广泛的无线电传播测量 (N=17,269).
- 在九种配置中,走廊宽度和发射器高度的系统变化.
- 开发一个混合线性+XGBoost框架,将物理见解与机器学习结合起来.
主要成果:
- 标准的近距离 (CI) 模型显示显著的几何依赖变化 (RMSE=3.91dB).
- 混合模型比CI模型实现了24.0%的改进 (RMSE=2.97dB).
- 混合模型与纯ML方法相比,对未见的几何配置进行了优越的概括.
结论:
- 果园的几何形状,特别是走廊的宽度,是无线电传播和阴影色的关键因素.
- 为了在农业中准确的道模型,需要明确纳入几何参数.
- 混合物理-ML架构为复杂环境中的无线通道建模提供了强大的概括.
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