• Register
  • Login

European Journal of Molecular & Clinical Medicine

  1. Home
  2. Identification and Detection of Plant Diseases by Convolutional Neural Networks

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Identification and Detection of Plant Diseases by Convolutional Neural Networks

    Authors

    • A. Iyswariya 1
    • V. Ramkumar 2
    • Sarvepalli Chandrasekhar 3
    • Yaddala Chandrasekhar Reddy 3
    • Vunnam Sai Tathwik 3
    • V.Praveen Kumar 4

    1 R.M.K. Engineering College ,

    2 R.M.K. Engineering College ,Ltd

    3 R.M.K. Engineering College

    4 Nagman Instruments and Electronics Pvt.Ltd

,

Document Type : Research Article

  • Article Information
  • Download
  • Export Citation
  • Statistics
  • Share

Abstract

Agribusiness is the foundation of Indian economy. Plant health and food safety goes hand in hand. The health of green plants is of vital importance to everyone.Plant diseases being an impairment to the normal state of a plant, it interrupts or modifies plants vital functions. The proposed system helps in identification of plant disease and provides remedies that can be used as a defense mechanism against the disease. The database obtained from the Internet is properly segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy and confidence level of the project .Then using training data we will train our classifier and then output will be predicted with optimum accuracy. We use Convolution Neural Network (CNN) which comprises of different layers are used for prediction.CNNs provide unparalleled performance in tasks related to the classification and detection of crop diseases. They are able to manage complex issues in difficult imaging conditions A prototype drone model is also designed which can be used for live coverage of large agricultural fields to which a high resolution camera is attached and will capture images of the plants which will act as input for the software, based of which the software will tell us whether the plant is healthy or not. With our code and training model we have achieved an accuracy level of 78%. Our software gives us the name of the plant species with its confidence level and also the remedy that can be taken as a cure.

Keywords

  • Convolution neural networks
  • Diseases
  • Feature Extraction
  • agriculture
  • crops
  • Image classification
  • Object detection
  • XML
  • PDF 344.98 K
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
    • Article View: 245
    • PDF Download: 630
European Journal of Molecular & Clinical Medicine
Volume 7, Issue 4
November 2020
Page 2200-2205
Files
  • XML
  • PDF 344.98 K
Share
Export Citation
  • RIS
  • EndNote
  • Mendeley
  • BibTeX
  • APA
  • MLA
  • HARVARD
  • VANCOUVER
Statistics
  • Article View: 245
  • PDF Download: 630

APA

Iyswariya, A., Ramkumar, V., Chandrasekhar, S., Reddy, Y. C., Tathwik, V. S., & Kumar, V. (2020). Identification and Detection of Plant Diseases by Convolutional Neural Networks. European Journal of Molecular & Clinical Medicine, 7(4), 2200-2205.

MLA

A. Iyswariya; V. Ramkumar; Sarvepalli Chandrasekhar; Yaddala Chandrasekhar Reddy; Vunnam Sai Tathwik; V.Praveen Kumar. "Identification and Detection of Plant Diseases by Convolutional Neural Networks". European Journal of Molecular & Clinical Medicine, 7, 4, 2020, 2200-2205.

HARVARD

Iyswariya, A., Ramkumar, V., Chandrasekhar, S., Reddy, Y. C., Tathwik, V. S., Kumar, V. (2020). 'Identification and Detection of Plant Diseases by Convolutional Neural Networks', European Journal of Molecular & Clinical Medicine, 7(4), pp. 2200-2205.

VANCOUVER

Iyswariya, A., Ramkumar, V., Chandrasekhar, S., Reddy, Y. C., Tathwik, V. S., Kumar, V. Identification and Detection of Plant Diseases by Convolutional Neural Networks. European Journal of Molecular & Clinical Medicine, 2020; 7(4): 2200-2205.

  • Home
  • About Journal
  • Editorial Board
  • Submit Manuscript
  • Contact Us
  • Glossary
  • Sitemap

News

 

For Special Issue Proposal : editor.ejmcm21@gmail.com

Newsletter Subscription

Subscribe to the journal newsletter and receive the latest news and updates

© Journal Management System. Powered by ejournalplus