The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2021.0120125
PDF

Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection

Author 1: Radhika Bhagwat
Author 2: Yogesh Dandawate

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 1, 2021.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Agriculture has a dominant role in the world’s economy. However, losses due to crop diseases and pests significantly affect the contribution made by the agricultural sector. Plant diseases and pests recognized at an early stage can help limit the economic losses in agriculture production around the world. In this paper, a comprehensive multilayer convolutional neural network (CMCNN) is developed for plant disease detection that can analyze the visible symptoms on a variety of leaf images like, laboratory images with a plain background, complex images with real field conditions and images of individual disease symptoms or spots. The model performance is evaluated on three public datasets -Plant Village repository having images of the whole leaf with plain background, Plant Village repository with complex background and Digipathos repository with images of lone lesions and spots. Hyperparameters like learning rate, dropout probability, and optimizer are fine-tuned such that the model is capable of classifying various types of input leaf images. The overall classification accuracy of the model in handling laboratory images is 99.85%, real field condition images is 98.16% and for images with individual disease symptoms is 99.6%. The proposed design is also compared with the popular CNN architectures like GoogleNet, VGG16, VGG19 and ResNet50. The experimental results indicate that the suggested generic model has higher robustness in handling various types of leaf images and has better classification capability for plant disease detection. The obtained results suggest the favorable use of the proposed model in a decision support system to identify diseases in several plant species for a large range of leaf images.

Keywords: Crop diseases; plant disease detection; hyperparameters; deep learning; convolutional neural network

Radhika Bhagwat and Yogesh Dandawate, “Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 12(1), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120125

@article{Bhagwat2021,
title = {Comprehensive Multilayer Convolutional Neural Network for Plant Disease Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120125},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120125},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {1},
author = {Radhika Bhagwat and Yogesh Dandawate}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org