{"title":"基于主成分分析和支持向量机的小麦害虫识别系统","authors":"Jian Li, Lijuan Wang, Y. Li","doi":"10.1109/ICDMA.2012.217","DOIUrl":null,"url":null,"abstract":"An identification system of wheat pests is established by the combination of PCA (Principal Component Analysis) and SVM (Support Vector Machine) in this paper. Here PCA is used to extract image features on the wheat pests and SVM is used to identify classification on the feature vectors. It is shown that the system can get better identification efficiency, which can reach an identification rate of 81.25%. The effectiveness of this method is verified by MATLAB simulation experiments.","PeriodicalId":393655,"journal":{"name":"International Conference on Digital Manufacturing and Automation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Identification System of Wheat Pests Based on PCA and SVM\",\"authors\":\"Jian Li, Lijuan Wang, Y. Li\",\"doi\":\"10.1109/ICDMA.2012.217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An identification system of wheat pests is established by the combination of PCA (Principal Component Analysis) and SVM (Support Vector Machine) in this paper. Here PCA is used to extract image features on the wheat pests and SVM is used to identify classification on the feature vectors. It is shown that the system can get better identification efficiency, which can reach an identification rate of 81.25%. The effectiveness of this method is verified by MATLAB simulation experiments.\",\"PeriodicalId\":393655,\"journal\":{\"name\":\"International Conference on Digital Manufacturing and Automation\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Digital Manufacturing and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMA.2012.217\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Digital Manufacturing and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMA.2012.217","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Identification System of Wheat Pests Based on PCA and SVM
An identification system of wheat pests is established by the combination of PCA (Principal Component Analysis) and SVM (Support Vector Machine) in this paper. Here PCA is used to extract image features on the wheat pests and SVM is used to identify classification on the feature vectors. It is shown that the system can get better identification efficiency, which can reach an identification rate of 81.25%. The effectiveness of this method is verified by MATLAB simulation experiments.
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