Amarsinh Varpe, Yogesh D. Rajendra, Amol D. Vibhute, S. Gaikwad, K. Kale
{"title":"利用非成像高光谱数据识别植物物种","authors":"Amarsinh Varpe, Yogesh D. Rajendra, Amol D. Vibhute, S. Gaikwad, K. Kale","doi":"10.1109/MAMI.2015.7456613","DOIUrl":null,"url":null,"abstract":"Hyperspectral non-imaging data provides the spectral range from 400-2500nm which has the ability to identify each and every unique materials on the surface. The plant species identification is critical task manually and computationally. In the present paper, we have proposed plant species identification system based on non-imaging hyperspectral data and designed our own database for experiment. Also we have identified various plant species and performed support vector machine (SVM) algorithm on it for recognition. The overall accuracy 91% was achieved through SVM.","PeriodicalId":108908,"journal":{"name":"2015 International Conference on Man and Machine Interfacing (MAMI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Identification of plant species using non-imaging hyperspectral data\",\"authors\":\"Amarsinh Varpe, Yogesh D. Rajendra, Amol D. Vibhute, S. Gaikwad, K. Kale\",\"doi\":\"10.1109/MAMI.2015.7456613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperspectral non-imaging data provides the spectral range from 400-2500nm which has the ability to identify each and every unique materials on the surface. The plant species identification is critical task manually and computationally. In the present paper, we have proposed plant species identification system based on non-imaging hyperspectral data and designed our own database for experiment. Also we have identified various plant species and performed support vector machine (SVM) algorithm on it for recognition. The overall accuracy 91% was achieved through SVM.\",\"PeriodicalId\":108908,\"journal\":{\"name\":\"2015 International Conference on Man and Machine Interfacing (MAMI)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Man and Machine Interfacing (MAMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MAMI.2015.7456613\",\"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 International Conference on Man and Machine Interfacing (MAMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MAMI.2015.7456613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of plant species using non-imaging hyperspectral data
Hyperspectral non-imaging data provides the spectral range from 400-2500nm which has the ability to identify each and every unique materials on the surface. The plant species identification is critical task manually and computationally. In the present paper, we have proposed plant species identification system based on non-imaging hyperspectral data and designed our own database for experiment. Also we have identified various plant species and performed support vector machine (SVM) algorithm on it for recognition. The overall accuracy 91% was achieved through SVM.