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集成的微波腔扰动传感器用于对谷物的水分含量进行非破坏性估计:小麦和小豆的案例研究

Kamil Sacilik¹, Necati Cetin¹, Burak Ozbey²

¹Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, Ankara, Türkiye.

|March 5, 2026

概括

一个新的低成本微波传感器可以在没有昂贵的设备的情况下准确地估计谷物的水分含量. 像Bagging和k-NN这样的机器学习模型在分类谷物类型方面显示出高精度和100%的准确性.

科学领域:

农业工程农业工程
传感器技术传感器技术
微波工程微波工程

背景情况:

准确的水分含量 (MC) 估计对于谷物质量和储存至关重要.
传统方法通常依赖于昂贵的矢量网络分析器 (VNA),并且可能耗时.
开发具有成本效益的,非破坏性的实时监控技术至关重要.

研究的目的:

介绍一个低成本的,集成的微波腔扰动传感器,用于在小麦和小豆粒中进行非破坏性MC估计.
使用定制设计的电路开发密度独立的水分含量函数,M(Ψ).
评估各种机器学习 (ML) 模型的性能,用于MC预测和谷物类型分类.

主要方法:

使用微波腔扰动传感器在TM010模式下以2.45 GHz运行.
导出密度独立的水分含量函数,M(Ψ),避免了对VNAs的需要.
训练并比较多个ML模型,包括包装 (BAG),k-最近邻居 (k-NN) 和减少错误修剪树 (REPTree),与深度学习模型.

主要成果:

包装 (BAG) 模型表现出优异的预测性能,具有高的相关系数 (小麦R=0.995,豆R=0.989) 和低的根平均平方误差 (RMSE).
具体的RMSE值为小麦的0.207%,小的0.302%.
k-NN变种在分类谷物类型 (小麦与小豆) 中实现了完美的100%准确性.

结论:

拟议的集成微波传感器为实时谷物质量监测提供了精确,快速和具有成本效益的解决方案.
机器学习模型,特别是像Bagging这样的集体方法和k-NN这样的基于实例的学习,对于MC估计和谷物分类非常有效.
这种技术比传统方法有了显著的进步,增强了农业实践.

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