{"title":"基于深度聚类的植物病害分割网络","authors":"Seong-Eui Lee, Sang-Ho Lee, Jong-Ok Kim","doi":"10.1109/ICEIC57457.2023.10049898","DOIUrl":null,"url":null,"abstract":"Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.","PeriodicalId":373752,"journal":{"name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Deep-Clustering Based Plant Disease Segmentation Network\",\"authors\":\"Seong-Eui Lee, Sang-Ho Lee, Jong-Ok Kim\",\"doi\":\"10.1109/ICEIC57457.2023.10049898\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.\",\"PeriodicalId\":373752,\"journal\":{\"name\":\"2023 International Conference on Electronics, Information, and Communication (ICEIC)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Electronics, Information, and Communication (ICEIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEIC57457.2023.10049898\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIC57457.2023.10049898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep-Clustering Based Plant Disease Segmentation Network
Plant disease is a major factor that reduces the yield of plant cultivation. To solve this problem, many CNN-based disease detection models have been studied. However, existing methods focus on detecting disease regions of plants with a clean or constant background of image, so they are not practical in actual fields. Field images captured with UAVs frequently suffer from complex backgrounds. To overcome this problem, we propose a CNN-based plant disease segmentation network based on deep clustering.