{"title":"智能农业:利用卷积神经网络对生咖啡豆进行实时分类","authors":"Nen-Fu Huang, Dong-Lin Chou, Chia-An Lee, Feng-Ping Wu, An-Chi Chuang, Yi-Hsien Chen, Yin-Chun Tsai","doi":"10.1049/iet-smc.2020.0068","DOIUrl":null,"url":null,"abstract":"<div>\n <p>Coffee is an important economic crop and one of the most popular beverages worldwide. The rise of speciality coffees has changed people's standards regarding coffee quality. However, green coffee beans are often mixed with impurities and unpleasant beans. Therefore, this study aimed to solve the problem of time-consuming and labour-intensive manual selection of coffee beans for speciality coffee products. The second objective of the authors’ study was to develop an automatic coffee bean picking system. They first used image processing and data augmentation technologies to deal with the data. They then used deep learning of the convolutional neural network to analyse the image information. Finally, they applied the training model to connect an IP camera for recognition. They successfully divided good and bad beans. The false-positive rate was 0.1007, and the overall coffee bean recognition rate was 93%.</p>\n </div>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smc.2020.0068","citationCount":"11","resultStr":"{\"title\":\"Smart agriculture: real-time classification of green coffee beans by using a convolutional neural network\",\"authors\":\"Nen-Fu Huang, Dong-Lin Chou, Chia-An Lee, Feng-Ping Wu, An-Chi Chuang, Yi-Hsien Chen, Yin-Chun Tsai\",\"doi\":\"10.1049/iet-smc.2020.0068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>Coffee is an important economic crop and one of the most popular beverages worldwide. The rise of speciality coffees has changed people's standards regarding coffee quality. However, green coffee beans are often mixed with impurities and unpleasant beans. Therefore, this study aimed to solve the problem of time-consuming and labour-intensive manual selection of coffee beans for speciality coffee products. The second objective of the authors’ study was to develop an automatic coffee bean picking system. They first used image processing and data augmentation technologies to deal with the data. They then used deep learning of the convolutional neural network to analyse the image information. Finally, they applied the training model to connect an IP camera for recognition. They successfully divided good and bad beans. The false-positive rate was 0.1007, and the overall coffee bean recognition rate was 93%.</p>\\n </div>\",\"PeriodicalId\":34740,\"journal\":{\"name\":\"IET Smart Cities\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2020-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smc.2020.0068\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Smart Cities\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/iet-smc.2020.0068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Smart Cities","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/iet-smc.2020.0068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Smart agriculture: real-time classification of green coffee beans by using a convolutional neural network
Coffee is an important economic crop and one of the most popular beverages worldwide. The rise of speciality coffees has changed people's standards regarding coffee quality. However, green coffee beans are often mixed with impurities and unpleasant beans. Therefore, this study aimed to solve the problem of time-consuming and labour-intensive manual selection of coffee beans for speciality coffee products. The second objective of the authors’ study was to develop an automatic coffee bean picking system. They first used image processing and data augmentation technologies to deal with the data. They then used deep learning of the convolutional neural network to analyse the image information. Finally, they applied the training model to connect an IP camera for recognition. They successfully divided good and bad beans. The false-positive rate was 0.1007, and the overall coffee bean recognition rate was 93%.