{"title":"利用纹理特征和中K近邻对未成熟和成熟咖啡豆进行分类","authors":"Edwin R. Arboleda","doi":"10.37965/jait.2023.0203","DOIUrl":null,"url":null,"abstract":"In this study , texture features namely entropy, contrast, energy and homogeneity were extracted from mature and immature coffee beans using image processing and the values were inputted to MATLAB’s Classification Learner App for discrimination. Among the 23 machine learning algorithms the best performance was achieved by medium K nearest neighbor which has 97 % accuracy and 0.14574 seconds in speed. When compared to previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans, it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor\",\"authors\":\"Edwin R. Arboleda\",\"doi\":\"10.37965/jait.2023.0203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study , texture features namely entropy, contrast, energy and homogeneity were extracted from mature and immature coffee beans using image processing and the values were inputted to MATLAB’s Classification Learner App for discrimination. Among the 23 machine learning algorithms the best performance was achieved by medium K nearest neighbor which has 97 % accuracy and 0.14574 seconds in speed. When compared to previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans, it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.\",\"PeriodicalId\":70996,\"journal\":{\"name\":\"人工智能技术学报(英文)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"人工智能技术学报(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.37965/jait.2023.0203\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"人工智能技术学报(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.37965/jait.2023.0203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor
In this study , texture features namely entropy, contrast, energy and homogeneity were extracted from mature and immature coffee beans using image processing and the values were inputted to MATLAB’s Classification Learner App for discrimination. Among the 23 machine learning algorithms the best performance was achieved by medium K nearest neighbor which has 97 % accuracy and 0.14574 seconds in speed. When compared to previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans, it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.