检索结果-南通市图书馆

SVM and KNN Based CNN Architectures for Plant Classification

Computers, Materials & Continua 2022年第6期71卷 4257-4274页

作者： Sukanta Ghosh Amar Singh Kavita N.Z.Jhanjhi Mehedi Masud Sultan Aljahdali Lovely Professional University Jalandhar144005India Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia School of Computer Science and Engineering Tailor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automatic plant classification through plant leaf is a classical problem in Computer *** classification is challenging due to the introduction of new species with a similar pattern and *** efforts are made to automate plant classification using plant leaf,plant flower,bark,or *** much effort,it has been proven that leaf is the most reliable source for plant *** it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations,like sizes,textures,shapes,and ***,it is required to normalize all plant leaves into the same size to get better *** Neural Networks(CNN)provides a fair amount of accuracy when leaves are classified using this *** the performance can be improved by classifying using the traditional approach after applying *** this paper,two approaches,namely CNN+Support Vector Machine(SVM)and CNN+K-Nearest Neighbors(kNN)used on 3 datasets,namely LeafSnap dataset,Flavia Dataset,and MalayaKew *** datasets are augmented to take care all the *** assessments and correlations of the predetermined feature extractor models are ***+kNN managed to reach maximum accuracy of 99.5%,97.4%,and 80.04%,respectively,in the three datasets.

关键词： Plant leaf classification artificial intelligence SVM kNN deep learning deep CNN training epoch

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：