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

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Outstanding Reviewers

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
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • 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
  • RSS Feed

DOI: 10.14569/IJACSA.2025.0160830
PDF

Detection of Leaf Fall Disease in Sembawa Rubber Plantation Through Feature Extraction Model and Clustering Methods

Author 1: Alhadi Bustamam
Author 2: Devvi Sarwinda
Author 3: Retno Lestari
Author 4: Ahmad Ihsan Farhani
Author 5: Harum Ananda Setyawan
Author 6: Masita Dwi Mandini Manessa
Author 7: Tri Rappani Febbiyanti
Author 8: Minami Matsui

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 8, 2025.

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

Abstract: Natural rubber is one of Indonesia's most important export commodities, making the country the second-largest exporter globally with a 28.65% share of the world market. However, recent production has declined, partly due to leaf fall disease caused by the Pestalotiopsis sp. fungus. This disease leads to premature leaf drop, which forces rubber trees to redirect energy from latex production to leaf regeneration, potentially reducing yields by up to 30%. Traditional detection methods that rely on manual visual inspection of leaf morphology are impractical over large plantation areas. To address this, the present study proposes a remote sensing-based detection approach using aerial drone imagery and unsupervised machine learning. Two feature extraction methods: Convolutional Autoencoder (CAE) and Gray Level Co-occurrence Matrix (GLCM) were used prior to clustering with k-means. Despite a small dataset, the GLCM-based approach significantly outperforms the CAE-based method. These results demonstrate that GLCM combined with clustering can reliably distinguish between healthy and diseased plantation areas. The proposed method offers a cost-effective, scalable, and non-invasive alternative to ground surveys, and has strong potential for real-world deployment in disease monitoring and early warning systems across large agricultural regions.

Keywords: Convolutional autoencoder; gray level co-occurrence matrix; k-means clustering; rubber plant plantation; Pestalotiopsis sp

Alhadi Bustamam, Devvi Sarwinda, Retno Lestari, Ahmad Ihsan Farhani, Harum Ananda Setyawan, Masita Dwi Mandini Manessa, Tri Rappani Febbiyanti and Minami Matsui. “Detection of Leaf Fall Disease in Sembawa Rubber Plantation Through Feature Extraction Model and Clustering Methods”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.8 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160830

@article{Bustamam2025,
title = {Detection of Leaf Fall Disease in Sembawa Rubber Plantation Through Feature Extraction Model and Clustering Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160830},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160830},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {8},
author = {Alhadi Bustamam and Devvi Sarwinda and Retno Lestari and Ahmad Ihsan Farhani and Harum Ananda Setyawan and Masita Dwi Mandini Manessa and Tri Rappani Febbiyanti and Minami Matsui}
}



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

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 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

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

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

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.