Home > Published Issues > 2024 > Volume 12, No. 2, 2024 >
JOIG 2024 Vol.12(2): 137-144
doi: 10.18178/joig.12.2.137-144

Convolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in Indonesia

Rustam 1,*, Rita Noveriza 2, Siti Khotijah 3, Syamsul Rizal 1,4, Melati 2, Nor Kumalasari Caecar Pratiwi 1,5, Muhammad Hablul Barri 6, and Koredianto Usman 1
1. Department of Telecommunication Engineering, School of Electrical Engineering, Telkom University, Jawa Barat, Indonesia
2. Research Center for Horticultural and Estate Crops, National Research and Innovation Agency, Jakarta, Indonesia
3. Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Taiwan University of Science and Technology, Taiwan
4. Department of IT Convergence Engineering, College of Engineering, Kumoh National Institute of Technology, Gumi, South Korea
5. Department of Electronic Engineering, College of Engineering, Jeonbuk National University, Jeonju City, South Korea
6. Department of Biomedical Engineering, School of Electrical Engineering, Telkom University, Jawa Barat, Indonesia
Email: rustamtelu@telkomuniversity.ac.id (R.); rita_noveriza2000@yahoo.com (R.N.);
skhotijah0902@gmail.com (S.K.); syamsul@telkomuniversity.ac.id (S.R.); melatinazar@yahoo.co.id (M.); caecarnkcp@jbnu.ac.kr (N.K.C.P.); mhbarri@telkomuniversity.ac.id (M.H.B.); korediantousman@telkomuniversity.ac.id (K.U.)
*Corresponding author

Manuscript received September 19, 2023; revised November 14, 2023; accepted November 29, 2023; published April 10, 2024.

Abstract—Indonesia is the largest supplier of patchouli oil in the world market, contributing 80%–90%. Most patchouli oil products are exported in the perfume, cosmetics, pharmaceutical, antiseptic, aromatherapy, and insecticide industries. The emergence of patchouli leaf disease significantly reduced the production of wet, dry, oil, and patchouli alcohol. Therefore, selecting patchouli cuttings (seedlings) that are entirely healthy and disease-free is very important to prevent disease transmission from one area to another. In addition, the selection of disease-free seeds is also essential to prevent the use of diseased patchouli plant propagation. So far, the early identification of patchouli plant health is carried out through visual observations by experts using antiviral serum tested in the laboratory. However, this testing process is expensive. Therefore, in this paper, we proposed a novel Convolutional Neural Network (CNN) architecture for patchouli leaf diseases. We proposed a system for early identification of whether a patchouli leaf is diseased or healthy. Our CNN model uses three convolution layers, a dense layer, and a dropout layer. We compare the proposed model with well-known models, namely EfficientNetB0, AlexNet, InceptionV3, MobileNetV2, and VGG16. The results show that the proposed model outperformed five well-known models as a comparison. It has been confirmed by predicting the new and different testing data. This research contributes to the early identification of patchouli leaf diseases to reduce the expensive costs of identifying patchouli leaf diseases.

Keywords—leaf disease, patcholi, Indonesia, Convolutional Neural Network (CNN)

Cite: Rustam, Rita Noveriza , Siti Khotijah, Syamsul Rizal, Melati, Nor Kumalasari Caecar Pratiwi, Muhammad Hablul Barri, and Koredianto Usman, "Convolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in Indonesia," Journal of Image and Graphics, Vol. 12, No. 2, pp. 137-144, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

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