{"title":"Leaf Segmentation of Rosette Plants using Rough K-Means in CIELab Color Space","authors":"Arunita Das, Daipayan Ghosal, Krishna Gopal Dhal","doi":"10.18690/978-961-286-516-0.5","DOIUrl":null,"url":null,"abstract":"Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.","PeriodicalId":282591,"journal":{"name":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 7th Student Computer Science Research Conference (StuCoSReC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18690/978-961-286-516-0.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.
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