Wi-Pest:a method for detecting stored grain pests based on CSI
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 securi...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2020-12-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00186/ |
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| _version_ | 1841531183197847552 |
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| author | Shaowei SHAN Weidong YANG Le XIAO Ke WANG |
| author_facet | Shaowei SHAN Weidong YANG Le XIAO Ke WANG |
| author_sort | Shaowei SHAN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-91c2a43949e247658dfb7f6b0b3c6aa7 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2020-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-91c2a43949e247658dfb7f6b0b3c6aa72025-01-15T02:53:03ZzhoChina InfoCom Media Group物联网学报2096-37502020-12-014516159647289Wi-Pest:a method for detecting stored grain pests based on CSIShaowei SHANWeidong YANGLe XIAOKe WANGThe 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.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00186/CSIstored grain pest detectionamplituderandom forest classification |
| spellingShingle | Shaowei SHAN Weidong YANG Le XIAO Ke WANG Wi-Pest:a method for detecting stored grain pests based on CSI 物联网学报 CSI stored grain pest detection amplitude random forest classification |
| title | Wi-Pest:a method for detecting stored grain pests based on CSI |
| title_full | Wi-Pest:a method for detecting stored grain pests based on CSI |
| title_fullStr | Wi-Pest:a method for detecting stored grain pests based on CSI |
| title_full_unstemmed | Wi-Pest:a method for detecting stored grain pests based on CSI |
| title_short | Wi-Pest:a method for detecting stored grain pests based on CSI |
| title_sort | wi pest a method for detecting stored grain pests based on csi |
| topic | CSI stored grain pest detection amplitude random forest classification |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2020.00186/ |
| work_keys_str_mv | AT shaoweishan wipestamethodfordetectingstoredgrainpestsbasedoncsi AT weidongyang wipestamethodfordetectingstoredgrainpestsbasedoncsi AT lexiao wipestamethodfordetectingstoredgrainpestsbasedoncsi AT kewang wipestamethodfordetectingstoredgrainpestsbasedoncsi |