一种基于ESP32-CSI的粮食水分检测方法

A grain moisture detection method based on ESP32-CSI

为实现粮食水分的低成本快速准确测量,将小型化的信道状态信息(channel state information,CSI)采集设备用于粮食水分检测,采用随机森林和主成分分析两种特征选择算法对CSI的振幅指标进行特征子载波提取,基于选择的特征子载波对10种粮食水分进行分类,考虑到之后其移动化场景中的应用受限于功耗以及算力,选取结构较为简洁、运算速度较快、算力要求不高的宽度学习系统(broad learning system,BLS)应用于CSI数据的处理,同时与传统的卷积神经网络(convolutional neural network,CNN)在精确度和训练时间两个方面进行对比,最后动态地增加宽度学习系统的增强节点.试验结果表明:主成分分析(principal component analysis,PCA)算法最大限度地消除了CSI数据中的冗余信息,BLS相较于卷积神经网络不仅获得了更快的速度而且在准确率方面也优于CNN算法,因此PCA-BLS组合获得了最佳的分类效果;增加增强节点的数量后,训练时间虽然有所延长,但在一定程度上提高了识别准确率.

To realize fast and accurate measurement of grain moisture with low cost,the miniaturized channel state information(CSI)acquisition equipment was used for grain moisture detection.Two feature selection algorithms of random forest and principal component analysis were adopted to extract the feature subcarriers of the CSI amplitude index,and the ten kinds of grain moisture were classified based on the selected feature subcarriers.Considering that the application in the mobility scene was limited by power consumption and arithmetic power,the breadth learning system with simple structure,fast operation speed and low arithmetic power requirement was selected for processing CSI data and was compared with the traditional convolutional neural network(CNN)in terms of accuracy and training time.The enhancement nodes of the broad learning system(BLS)were dynamically increased.The experimental results show that the principal component analysis(PCA)algorithm maximally eliminates the redundant information in the CSI data.Compared with the CNN,the BLS can achieve faster speed and better accuracy.Therefore,the PCA-BLS combination achieves the best classification results.Increasing the number of enhancement nodes can increase the training time,but the recognition accuracy is improved to some extent.