{"title":"利用深度学习方法识别各种竹材病害","authors":"K. Kumar, Sachin Sharma, P. Pandey, H. Goyal","doi":"10.1109/IATMSI56455.2022.10119353","DOIUrl":null,"url":null,"abstract":"Bamboos with their enormous adaptations according to the environment, are used in almost all parts of the world. People often find these to be recognized as plants but instead, they belong to the family of grass. From ancient cultures to modern customization, they are always used. With that being said, these are the natural habitats that too suffer from diseases. Earlier papers were not available easily to the independent researcher and even there is less research on this field. The sole agenda of this paper is to provide all the answers related to the diseases which occur in bamboos. Firstly, we will check whether there is any disease in the sample taken, and then we will try to come up with some models to detect that disease. Here, we are taking the help of machine learning to determine the kind of disease. The Convolutional Neural Network model is used here for detection. Images have been used here as the data input for the training of the model, which Artificial Intelligence (AI) can easily process. This paper also represents the basic characteristics or properties of the diseases that can occur and how those will be distinguished. After all the citations of the earlier projects under this category, we have tried to come up with a solution that will be implemented accurately and efficiently.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Identification of Various Bamboo Diseases Using Deep Learning Approach\",\"authors\":\"K. Kumar, Sachin Sharma, P. Pandey, H. Goyal\",\"doi\":\"10.1109/IATMSI56455.2022.10119353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bamboos with their enormous adaptations according to the environment, are used in almost all parts of the world. People often find these to be recognized as plants but instead, they belong to the family of grass. From ancient cultures to modern customization, they are always used. With that being said, these are the natural habitats that too suffer from diseases. Earlier papers were not available easily to the independent researcher and even there is less research on this field. The sole agenda of this paper is to provide all the answers related to the diseases which occur in bamboos. Firstly, we will check whether there is any disease in the sample taken, and then we will try to come up with some models to detect that disease. Here, we are taking the help of machine learning to determine the kind of disease. The Convolutional Neural Network model is used here for detection. Images have been used here as the data input for the training of the model, which Artificial Intelligence (AI) can easily process. This paper also represents the basic characteristics or properties of the diseases that can occur and how those will be distinguished. After all the citations of the earlier projects under this category, we have tried to come up with a solution that will be implemented accurately and efficiently.\",\"PeriodicalId\":221211,\"journal\":{\"name\":\"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IATMSI56455.2022.10119353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IATMSI56455.2022.10119353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Various Bamboo Diseases Using Deep Learning Approach
Bamboos with their enormous adaptations according to the environment, are used in almost all parts of the world. People often find these to be recognized as plants but instead, they belong to the family of grass. From ancient cultures to modern customization, they are always used. With that being said, these are the natural habitats that too suffer from diseases. Earlier papers were not available easily to the independent researcher and even there is less research on this field. The sole agenda of this paper is to provide all the answers related to the diseases which occur in bamboos. Firstly, we will check whether there is any disease in the sample taken, and then we will try to come up with some models to detect that disease. Here, we are taking the help of machine learning to determine the kind of disease. The Convolutional Neural Network model is used here for detection. Images have been used here as the data input for the training of the model, which Artificial Intelligence (AI) can easily process. This paper also represents the basic characteristics or properties of the diseases that can occur and how those will be distinguished. After all the citations of the earlier projects under this category, we have tried to come up with a solution that will be implemented accurately and efficiently.