Mr. Srinath G M, Ms. Arfa Thareen K, Ms. Noor Fathima M, Ms. Vandana C K, Ms. Vinutha C R
{"title":"利用深度学习算法检测植物叶片病害","authors":"Mr. Srinath G M, Ms. Arfa Thareen K, Ms. Noor Fathima M, Ms. Vandana C K, Ms. Vinutha C R","doi":"10.48175/ijarsct-18475","DOIUrl":null,"url":null,"abstract":"The Plant Leaf Diseases Detection System addresses the critical challenge of early detection and management of plant diseases, significantly impacting agricultural productivity and food security. Utilizing advanced technologies, this cutting-edge agricultural solution employs a Convolutional Neural Network (CNN) model, specifically based on the VGG19 architecture implemented using Keras. This robust deep learning model is trained on a diverse dataset containing images of both healthy and diseased leaves, allowing it to extract intricate features and accurately classify various plant diseases automatically. The system seamlessly integrates HTML, CSS, and Flask for the front end, while Keras powers the back end, resulting in a user-friendly web application interface. Incorporating this technology not only enhances the efficiency of disease detection but also facilitates user interaction and accessibility","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"13 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Plant Leaf Disease Detection using Deep Learning Algorithms\",\"authors\":\"Mr. Srinath G M, Ms. Arfa Thareen K, Ms. Noor Fathima M, Ms. Vandana C K, Ms. Vinutha C R\",\"doi\":\"10.48175/ijarsct-18475\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Plant Leaf Diseases Detection System addresses the critical challenge of early detection and management of plant diseases, significantly impacting agricultural productivity and food security. Utilizing advanced technologies, this cutting-edge agricultural solution employs a Convolutional Neural Network (CNN) model, specifically based on the VGG19 architecture implemented using Keras. This robust deep learning model is trained on a diverse dataset containing images of both healthy and diseased leaves, allowing it to extract intricate features and accurately classify various plant diseases automatically. The system seamlessly integrates HTML, CSS, and Flask for the front end, while Keras powers the back end, resulting in a user-friendly web application interface. Incorporating this technology not only enhances the efficiency of disease detection but also facilitates user interaction and accessibility\",\"PeriodicalId\":472960,\"journal\":{\"name\":\"International Journal of Advanced Research in Science, Communication and Technology\",\"volume\":\"13 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Research in Science, Communication and Technology\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.48175/ijarsct-18475\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Research in Science, Communication and Technology","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.48175/ijarsct-18475","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
植物叶病检测系统解决了植物病害早期检测和管理的关键难题,对农业生产力和粮食安全产生了重大影响。利用先进技术,这一尖端农业解决方案采用了卷积神经网络(CNN)模型,特别是基于使用 Keras 实现的 VGG19 架构。这种强大的深度学习模型是在包含健康和病叶图像的各种数据集上训练出来的,使其能够提取复杂的特征并自动准确地对各种植物病害进行分类。该系统的前端无缝集成了 HTML、CSS 和 Flask,后端则由 Keras 驱动,从而形成了用户友好的网络应用程序界面。采用这项技术不仅能提高病害检测的效率,还能方便用户互动和访问
Plant Leaf Disease Detection using Deep Learning Algorithms
The Plant Leaf Diseases Detection System addresses the critical challenge of early detection and management of plant diseases, significantly impacting agricultural productivity and food security. Utilizing advanced technologies, this cutting-edge agricultural solution employs a Convolutional Neural Network (CNN) model, specifically based on the VGG19 architecture implemented using Keras. This robust deep learning model is trained on a diverse dataset containing images of both healthy and diseased leaves, allowing it to extract intricate features and accurately classify various plant diseases automatically. The system seamlessly integrates HTML, CSS, and Flask for the front end, while Keras powers the back end, resulting in a user-friendly web application interface. Incorporating this technology not only enhances the efficiency of disease detection but also facilitates user interaction and accessibility