Hassan Koroshi Talab, Davood Mohammadzamani, Mohammad Gholami Parashkoohi
{"title":"利用图像处理和机器学习研究非平衡数据分类的准确性,以诊断两种常见的马铃薯叶部病害(早疫病和晚疫病","authors":"Hassan Koroshi Talab, Davood Mohammadzamani, Mohammad Gholami Parashkoohi","doi":"10.1007/s42452-024-05959-2","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":517252,"journal":{"name":"Discover Applied Sciences","volume":"58 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning\",\"authors\":\"Hassan Koroshi Talab, Davood Mohammadzamani, Mohammad Gholami Parashkoohi\",\"doi\":\"10.1007/s42452-024-05959-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":517252,\"journal\":{\"name\":\"Discover Applied Sciences\",\"volume\":\"58 9\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discover Applied Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s42452-024-05959-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s42452-024-05959-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning