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Yana Hendriana
Universitas Nahdlatul Ulama Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Intechno Journal : Information Technology Journal

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An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection Marco, Robert; Muhammad, Alva Hendi; Aini, Nur; Hendriana, Yana
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2482

Abstract

Purpose: Accurate detection of paddy leaf diseases is essential to ensure optimal crop yield and effective disease management. Methods/Study design/approach: In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and an Attention mechanism for paddy leaf disease classification using the Paddy Doctor dataset. The CNN layers extract spatial features from leaf images, the LSTM captures contextual relationships between these features, and the Attention mechanism emphasizes the most relevant patterns for accurate classification. Result/Findings: Experimental results show that the proposed CNN+LSTM+Attention model achieves 95.5% accuracy, 98.12% precision, 98.3% recall, and 0.994 macro AUC, outperforming a simple CNN-3 layer while offering competitive performance compared to state-of-the-art architectures such as ResNet34 and Xception. Novelty/Originality/Value: These results demonstrate that the proposed model is highly effective in detecting paddy leaf diseases with minimal false negatives, providing a reliable and practical solution for automated paddy disease monitoring systems
Co-Authors Abadi, Anis Susila Abdal Rohim Abdulloh Badruzzaman Agus Sasmito Alva Hendi Muhammad Anastasia Lidya Maukar Apri Junaidi, Apri Bayu Taruno, Restiadi Damar Wicaksono Febriana Dewi, Pipit Gunawan Hashim, Ummi Rabah Heni Pujiastuti Heri Hermansyah Ilham Unggara Jack Febrian Rusdi Joang Ipmawati Kaidani, M. Kausari Kamalrudin, Massila Melvin Rahma Sayuga Subroto Mulyati, Lia Nur Aini Nur Azmi Ainul Bashir Pranatha, Aria Pratiwi, Nurna Rahma, Nimas Ristiya Restiadi Bayu Taruno Richki Hardi Robert Marco, Robert Rodhiyah Mardhiyyah Sari, Ade Mulya Septiadi, Dikri Sigit Sigalayan Suhada Suhada, Suhada SUMARDI Taruno, Restiadi Bayu Tatag Lindu Bhakti Taufiq Kamal unggara, Ilham Wahma Ravenna Rafail Yuliani, Devi Yustian Zahrotul Hasanah, Labibah Zulfatun Ruscitasari Zulkhairi