{"title":"原始槟榔图像的颜色特征和KNN分类","authors":"S. Siddesha, S. Niranjan, V. N. Manjunath Aradhya","doi":"10.1109/ICGCIOT.2018.8753075","DOIUrl":null,"url":null,"abstract":"Arecanut is one of the important cash crops of Southern India. Classification of raw arecanut is one of the major tasks in grading, which is a vital part of crop management. In this work we proposed a model which classifies the raw arecanut. We used color histogram and color moments as features with K-NN classifier. Experiment is conducted on a dataset of 800 images of four classes using two color features and four distance measures with K-NN. A classification accuracy of 98.13% is achieved for 20% training with K value of 3 and Euclidean distance measure for color histogram features.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Color Features and KNN in Classification of Raw Arecanut images\",\"authors\":\"S. Siddesha, S. Niranjan, V. N. Manjunath Aradhya\",\"doi\":\"10.1109/ICGCIOT.2018.8753075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Arecanut is one of the important cash crops of Southern India. Classification of raw arecanut is one of the major tasks in grading, which is a vital part of crop management. In this work we proposed a model which classifies the raw arecanut. We used color histogram and color moments as features with K-NN classifier. Experiment is conducted on a dataset of 800 images of four classes using two color features and four distance measures with K-NN. A classification accuracy of 98.13% is achieved for 20% training with K value of 3 and Euclidean distance measure for color histogram features.\",\"PeriodicalId\":269682,\"journal\":{\"name\":\"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)\",\"volume\":\"04 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICGCIOT.2018.8753075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICGCIOT.2018.8753075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Color Features and KNN in Classification of Raw Arecanut images
Arecanut is one of the important cash crops of Southern India. Classification of raw arecanut is one of the major tasks in grading, which is a vital part of crop management. In this work we proposed a model which classifies the raw arecanut. We used color histogram and color moments as features with K-NN classifier. Experiment is conducted on a dataset of 800 images of four classes using two color features and four distance measures with K-NN. A classification accuracy of 98.13% is achieved for 20% training with K value of 3 and Euclidean distance measure for color histogram features.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
