Wi-Pest：一种基于CSI的储粮害虫检测方法

单少伟; 杨卫东; 肖乐; 王珂

doi:10.11959/j.issn.2096-3750.2020.00186

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Wi-Pest：一种基于CSI的储粮害虫检测方法

专题：智慧农业 | 更新时间：2024-06-05

- Wi-Pest：一种基于CSI的储粮害虫检测方法
- Wi-Pest:a method for detecting stored grain pests based on CSI
- 物联网学报 2020年4卷第4期页码：51-61
- 作者机构：
  
  1. 河南工业大学信息科学与工程学院，河南郑州 450066
  2. 粮食信息处理与控制教育部重点实验室（河南工业大学），河南郑州 450066
- 作者简介：
  
  [ "单少伟（1992- ），男，河南郑州人，河南工业大学硕士生，主要研究方向为无线网络、物联网等" ]
  [ "杨卫东（1977- ），男，河南郑州人，博士，河南工业大学教授，主要研究方向为无线网络安全、隐私保护、车载自组织网络、物联网、车联网等" ]
  [ "肖乐（1972- ），男，河南开封人，河南工业大学副教授，主要研究方向为人工智能、智能计算等" ]
  [ "王珂（1980- ），男，河南郑州人，河南工业大学讲师，主要研究方向为大数据、物联网" ]
- 基金信息：
  
  国家重点研发计划;The National Key R＆D Program of China(2017YFD0401001);国家自然科学基金资助项目;The National Natural Science Foundation of China(61741107);河南省省属高校基本科研业务费专项资金资助项目;The Fundamental Research Funds for the Henan Provincial Colleges and Universities(2018RCJH12);河南工业大学科研平台开放课题;The Open Fund for Henan University of Technology(KFJJ-2016-202)
- DOI：10.11959/j.issn.2096-3750.2020.00186
  中图分类号： TN92
- 纸质出版日期：2020-12-30，
  
  网络出版日期：2020-12，
- 稿件说明：
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单少伟, 杨卫东, 肖乐, 等. Wi-Pest：一种基于CSI的储粮害虫检测方法[J]. 物联网学报, 2020,4(4):51-61.

SHAOWEI SHAN, WEIDONG YANG, LE XIAO, et al. Wi-Pest:a method for detecting stored grain pests based on CSI. [J]. Chinese journal on internet of things, 2020, 4(4): 51-61.
单少伟, 杨卫东, 肖乐, 等. Wi-Pest：一种基于CSI的储粮害虫检测方法[J]. 物联网学报, 2020,4(4):51-61. DOI： 10.11959/j.issn.2096-3750.2020.00186.

SHAOWEI SHAN, WEIDONG YANG, LE XIAO, et al. Wi-Pest:a method for detecting stored grain pests based on CSI. [J]. Chinese journal on internet of things, 2020, 4(4): 51-61. DOI： 10.11959/j.issn.2096-3750.2020.00186.

摘要

在粮食储藏过程中，影响粮食安全的环境和生物因素，如粮食温度、环境湿度、水分、气体、霉变、害虫等，均会对粮食储藏安全构成威胁，其中害虫是威胁粮食储藏安全的一个重要因素。因此，需要研究一种快速且有效的检测方法，用于检测储粮害虫。现有的一些方法耗时、设备昂贵、具有潜在的健康危害并且检测效率较低，提出了一种基于信道状态信息（CSI

channel state information）振幅的非接触、快速、低成本的储粮害虫检测方法，即无线测害虫（Wi-Pest

wireless-pest）。通过使用CSI振幅数据验证储粮害虫检测的可行性，在此基础上，设计了Wi-Pest检测方法。首先对CSI振幅数据进行异常值检测、数据归一化和噪声消除预处理，然后通过主成分分析（PCA

principal component analysis）方法压缩数据并提取主特征成分，最后采用随机森林（RF

random forest）分类方法检测储粮害虫。实验结果表明，所提方法在视距（LOS

line of sight）场景下，能够检测粮堆活体害虫密度的异常情况，检测精度平均可以达到97%。

Abstract

The environmental and biological factors that affect the food security during the food storage

such as the food temperature

environment humidity

moisture

gas

mildew

pests and others pose a threat to the food storage security

among which the pest is an important factor threatening food storage security.Therefore

a fast and effective detection method is needed to detect stored grain pests.Some of the existing methods are time consuming

using expensive equipment

potentially harmful to health and inefficient.A non-contact

fast and low-cost detection method for stored grain pests based on the amplitude of the channel state information (CSI) was proposed

namely

wireless-pest (Wi-Pest).The feasibility of the pest detection in the stored grain was verified by using CSI amplitude data.On this basis

a Wi-Pest detection method was designed.Firstly

the amplitude data of CSI was preprocessed by outliers removal

data normalization and noise elimination.Then the principal component analysis (PCA) was used to compress the data and extract the main feature components.Finally

random forest (RF) classification method was used to detect stored grain pests.Experiments show that the abnormal density of live pests in grain heaps can be detected under the line of sight (LOS) scenario

and the detection accuracy of the proposed method can reach 97% on average.

关键词

CSI储粮害虫检测振幅随机森林分类

Keywords

CSIstored grain pest detectionamplituderandom forest classification

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