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

Publication Links

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

IJACSA

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

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

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
  • Indexing
  • Submit your Paper
  • Guidelines
  • Fees
  • Current Issue
  • Archives
  • Editors
  • Reviewers
  • Subscribe

Article Details

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.

Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u

Author 1: Erbert F. Osco-Mamani
Author 2: Israel N. Chaparro-Cruz

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2023.0140494

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

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

Abstract: Deep learning applied to computer vision has different applications in agriculture, medicine, marketing, meteorology, etc. In agriculture, plant diseases can cause significant yield and quality losses. The treatment of these diseases depends on accurate and rapid classification. Olive leaf diseases are a problem that threatens the crop quality of olive growers. The objective of this work was to classify olive leaf diseases with Deep Learning in olive crops of the La Yarada-Los Palos area in the Tacna region, Peru. Disease classification is a critical task, nevertheless, for the most common diseases in the region: virosis, fumagina, and nutritional deficiencies, there is no dataset to train deep learning models. Due to the latter, a novel dataset of RGB olive leaf images is elaborated and published. Then, an extensive comparative ex-perimental study was conducted using all possible configurations of Learning from Scratch, Transfer Learning, Fine-Tuning, and Data Augmentation state-of-the-art methods to train a modified VGG16 architecture for the classification of Olive Leaf Diseases. It was demonstrated experimentally: (i) The ineffectiveness of Data Augmentation when the model Learning from Scratch, (ii) A high improvement by using Transfer Learning vs Learning from Scratch, (iii) Similar performance using Transfer Learning vs Transfer Learning + Fine-Tuning vs Transfer Learning + Data Augmentation, and (iv) Very high improvement using Transfer Learning + Fine-Tuning + Data Augmentation. This led us to a Deep Learning Model with an accuracy of 100%, 99.93%, and 100% in the training, validation, and test sets and F1-Score on the validation set of 1, 0.9901, and 0.9899 in the Nutritional Deficiences, Fumagina, and Virosis olive leaf diseases respectively. Replication of the results is ensured by publishing the novel dataset and the final model on GitHub.

Keywords: Olive; leaf diseases; disease classification; deep learning; data augmentation; transfer learning; fine-tuning; VGG16

Erbert F. Osco-Mamani and Israel N. Chaparro-Cruz, “Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140494

@article{Osco-Mamani2023,
title = {Highly Accurate Deep Learning Model for Olive Leaf Disease Classification: A Study in Tacna-Per´u},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140494},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140494},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Erbert F. Osco-Mamani and Israel N. Chaparro-Cruz}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2023

2-3 March 2023

  • Virtual

Computing Conference 2023

22-23 June 2023

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
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