Plant Leaf Disease Detection and Classification using Optimized CNN Model

S. Prabavathi, P. Kanmani
{"title":"Plant Leaf Disease Detection and Classification using Optimized CNN Model","authors":"S. Prabavathi, P. Kanmani","doi":"10.35940/IJRTE.F5572.039621","DOIUrl":null,"url":null,"abstract":"Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.","PeriodicalId":220909,"journal":{"name":"International Journal of Recent Technology and Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Recent Technology and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35940/IJRTE.F5572.039621","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Our economy depends on productivity in agriculture. The quantity and quality of the yield is greatly affected by various hazardous diseases. Early-stage detection of plant disease will be very helpful to prevent severe damage. Automatic systems to detect the changes in the plants by monitoring the abnormal symptoms in its growth will be more beneficial for the farmers. This paper presents a system for automatic prediction and classification of plant leaf diseases. The survey on various diseases classification techniques that can be used for plant leaf disease detection are also discussed. The proposed system will define the cropped image of a plant through image processing and feature extraction algorithms. Enhanced CNN model is designed and applied for about 20,600 images are collected as a dataset. Optimization is done to enhance the accuracy in the system prediction and to show the improvement in the true positive samples classification. The proposed system shows the improvement in the accuracy of prediction as 93.18% for three different species with twelve different diseases.
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
基于优化CNN模型的植物叶片病害检测与分类
我们的经济依赖于农业生产力。产量的数量和质量受各种危害病害的影响很大。植物病害的早期检测将有助于防止严重的危害。通过监测植物生长中的异常症状来检测植物变化的自动化系统将对农民更有利。提出了一种植物叶片病害自动预测与分类系统。对植物叶片病害检测的各种病害分类技术进行了综述。该系统将通过图像处理和特征提取算法来定义植物的裁剪图像。针对收集到的约20,600张图像作为数据集,设计并应用了增强CNN模型。通过优化,提高了系统预测的准确性,并在真正样本分类方面取得了进步。该系统对3种不同物种、12种不同病害的预测准确率提高了93.18%。
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
Software Defect Estimation using Machine Learning Algorithms Plant Leaf Disease Detection and Classification using Optimized CNN Model Industrial Internet of Things (IIoT) of Forensic and Vulnerabilities Stabilization of Black cotton soil using Fly ash Effect of Admixing Fly Ash on Cementing Characteristics of Magnesium Oxychloride Cement
×
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