Prediction of Brinjal Plant Disease Using Support Vector Machine and Convolutional Neural Network Algorithm Based on Deep Learning

Venkataramana Attada, K. Kumar, N. Suganthi, R. Rajeswari
{"title":"Prediction of Brinjal Plant Disease Using Support Vector Machine and Convolutional Neural Network Algorithm Based on Deep Learning","authors":"Venkataramana Attada, K. Kumar, N. Suganthi, R. Rajeswari","doi":"10.13052/jmm1550-4646.18315","DOIUrl":null,"url":null,"abstract":"Plant pathogens prediction is the prerequisite for timely and productive control of plant pathogens within complicated environments. However, the white mold is a complicated disease in a brinjal plant. Hence, to vanquish these difficulties a novel Deep Learning Integration (DLI) Techniques has been proposed. In Proposed system, classification is carried out by Support Vector Machine (SVM) and prediction is carried out by Convolutional Neural Network (CNN) Algorithm to predict the plant illness in Brinjal with high accuracy of 99.4%.","PeriodicalId":425561,"journal":{"name":"J. Mobile Multimedia","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Mobile Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/jmm1550-4646.18315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Plant pathogens prediction is the prerequisite for timely and productive control of plant pathogens within complicated environments. However, the white mold is a complicated disease in a brinjal plant. Hence, to vanquish these difficulties a novel Deep Learning Integration (DLI) Techniques has been proposed. In Proposed system, classification is carried out by Support Vector Machine (SVM) and prediction is carried out by Convolutional Neural Network (CNN) Algorithm to predict the plant illness in Brinjal with high accuracy of 99.4%.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于深度学习的支持向量机和卷积神经网络算法预测茄子病害
植物病原体预测是在复杂环境中及时有效控制植物病原体的前提。然而,白霉病是茄子植物中的一种复杂病害。因此,为了克服这些困难,提出了一种新的深度学习集成(DLI)技术。该系统采用支持向量机(SVM)进行分类,卷积神经网络(CNN)算法进行预测,对茄子病害进行预测,准确率高达99.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
The Disruptive Innovation Potential and Business Case Investment Sensitivity of Open RAN Live Streaming Contents Influencing Game Playing Behavior Among Thailand Gamers Hyperledger Fabric-based Reliable Personal Health Information Sharing Model A Conceptual Model of Personalized Virtual Reality Trail Running Gamification Design Protein Prediction using Dictionary Based Regression Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1