{"title":"不同种类植物的分类和灌溉与移动应用","authors":"E. Yalcin, Derya Yiltas-Kaplan","doi":"10.1109/UBMK52708.2021.9559029","DOIUrl":null,"url":null,"abstract":"In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and Irrigation of Different Kinds of Plants with Mobile Application\",\"authors\":\"E. Yalcin, Derya Yiltas-Kaplan\",\"doi\":\"10.1109/UBMK52708.2021.9559029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.\",\"PeriodicalId\":106516,\"journal\":{\"name\":\"2021 6th International Conference on Computer Science and Engineering (UBMK)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 6th International Conference on Computer Science and Engineering (UBMK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UBMK52708.2021.9559029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK52708.2021.9559029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification and Irrigation of Different Kinds of Plants with Mobile Application
In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.