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All Journal International Journal of Electrical and Computer Engineering Althea Medical Journal TheJournalish: Social and Government GOVERNABILITAS (Jurnal Ilmu Pemerintahan Semesta) JOELS: Journal of Election and Leadership Journal of the Community Development in Asia Journal of Scientific Research, Education, and Technology Jurnal Pengabdian kepada Masyarakat AL-SULTHANIYAH PARADUTA : Jurnal Ekonomi dan Ilmu-Ilmu Sosial Journal Emerging Technologies in Education Computing Insight: Journal of Computer Science Jurnal Riset Teknik Komputer Intechno Journal : Information Technology Journal Jurnal Padamu Negeri PESHUM Journal of Innovation and Computer Science Public Knowledge
Mohamad Firdaus, Mohamad
Faculty of Medicine Universitas Padjadjaran
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Optimizing rice leaf disease classification through convolutional neural network architectural modification and augmentation techniques Firdaus, Mohamad; Kusrini, Kusrini; Agastya, I Made Artha; Martínez-Béjar, Rodrigo
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3429-3438

Abstract

This research focuses on advancing the accuracy of rice leaf disease classification through the integration of convolutional neural network (CNN) and deep learning models. With Indonesia ranking third in global rice production, effective crop management is crucial for sustaining agricultural output. This study employs innovative data augmentation techniques, including random zoom and others, to enhance model training robustness. The experimentation involves eight scenarios with varied architectural configurations applied to a residual network-50 (ResNet50) layers model, aiming to optimize disease classification performance. Featuring random zoom without the multilayer perceptron (MLP) component, emerges as the most effective, demonstrating superior accuracy and performance metrics. A grid search is conducted to optimize MLP layers, revealing a three-layer configuration as most effective. We found that the data augmentation and MLP layer can increase the accuracy of the disease classification task. The method proposed in this study is likely to have a much higher proportion of correct disease classification by combining MLP and zoom augmentation. Specifically, the model with three MLP layers and zoom augmentation demonstrated significantly higher accuracy, achieving a test accuracy, precision, recall, and F1-score of 0.92, 0.94, 0.92, and 0.92, respectively.
Co-Authors Abdul Aziz Abdul Halik Agastya, I Made Artha Ahyani Alkhalaf, Lulwa Arief Hidayat Azmi, Mohd Danieal, Danieal Etebom, John Monday Faai Faujan, Tubagus Fatoni Haryati Herliawan, Aditya Indra Bakti Kee, Daisy Mui Hung KUSRINI Kusrini, Kusrini M. Rudyanto Arief Martínez-Béjar, Rodrigo Mayang Sari Meisuri Nani Murniati Novita, Frinda Nucki Nursjamsi Hidajat, Nucki Nursjamsi Nurafiyani, Desi Nuraini, Idzma Rahmadi, R Y Gembong Rahmadi, Raden Yoseph Gembong Ramadani, Muhammad Pazri Sahdan, Gregorius Siregar, Yuliza Andriyani Sulistiana, Utami Supiati Tripathi, Abhishek Wijaya, Junior Hendri Yuni Lestari, Ria Zidni Hudaya, Ahmad