{"title":"基于隐式Naïve Bays分类器的叶片植物识别系统","authors":"Heba F. Eid, A. Hassanien, Tai-hoon Kim","doi":"10.1109/AITS.2015.28","DOIUrl":null,"url":null,"abstract":"Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.","PeriodicalId":196795,"journal":{"name":"2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Leaf Plant Identification System Based on Hidden Naïve Bays Classifier\",\"authors\":\"Heba F. Eid, A. Hassanien, Tai-hoon Kim\",\"doi\":\"10.1109/AITS.2015.28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.\",\"PeriodicalId\":196795,\"journal\":{\"name\":\"2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AITS.2015.28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AITS.2015.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leaf Plant Identification System Based on Hidden Naïve Bays Classifier
Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.