Wi-Wheat: Contact-Free Wheat Moisture Detection with Commodity WiFi
Wi-Wheat: Contact-Free Wheat Moisture Detection with Commodity WiFi
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DOI：
10.1109/icc.2018.8423034
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Weidong Yang;Xuyu Wang;Anxiao Song;S. Mao
中科院分区：
文献类型：
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作者：
Weidong Yang;Xuyu Wang;Anxiao Song;S. Mao
本文提出了一种基于商品WiFi的小麦水分无损检测系统。首先，我们通过实验验证了利用CSI幅值和相位差数据检测小麦水分的可行性。在此基础上设计了Wi-Wheat系统，CSI处理模块实现了数据预处理、特征提取和支持向量机分类。在数据预处理方面，采用了异常点检测、数据归一化和去噪等方法，得到了清晰的CSI幅度和相位差数据。然后，我们考虑基于主成分分析（PCA）的Wi-Wheat系统的特征提取。在支持向量机分类中，采用高斯径向基函数（RBF）作为小麦水分检测的核函数。实验结果表明，Wi-Wheat系统在LOS和NLOS场景下均能达到较高的分类精度。
In this paper, we present a non-destructive and economic wheat moisture detection system with commodity WiFi. First, we experimentally validate the feasibility of wheat moisture detection by using CSI amplitude and phase difference data. We then design Wi-Wheat system, where data preprocessing, feature extraction and support vector machine (SVM) classification are implemented for CSI processing module. For data preprocessing, we employ outlier detection, data normalization and eliminating noise for obtaining clear CSI amplitude and phase difference data. Then, we consider principal component analysis (PCA) based feature extraction for Wi-Wheat system. For SVM classification, Gaussian radial basis function (RBF) is used as the kernel function for wheat moisture detection. The experimental results show the Wi-Wheat system can achieve higher classification accuracy for LOS and NLOS scenarios.