Research Article
Open Access
Classification of Rice Leaf Diseases Based on Lightweight Deep Learning Model
1
Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811726, Taiwan
* Corresponding Author:
Chih-Chin Lai,
[email protected]
Volume 3, Issue 2
2026
Received: 06 December 2025
Accepted: 20 April 2026
Published: 26 May 2026
Article Information
Abstract
Traditional methods for classifying plant diseases usually depend on manual observation, which is time-consuming, labor-intensive, and prone to human error. The rise of deep learning has greatly advanced this field by enabling more accurate and efficient classification techniques. In this paper, we introduce a novel lightweight deep learning framework that builds on the RegNetY convolutional neural network architecture by incorporating a modified Efficient Channel Attention module. This enhancement is specifically designed to improve the classification of various rice leaf diseases. Our experiments on a publicly available dataset show that the proposed approach not only boosts classification accuracy but also significantly reduces computational complexity and memory usage, making it ideal for deployment on resource-limited edge devices.
Graphical Abstract
Keywords
rice leaf disease classification
deep learning
lightweight convolutional neural network
attention mechanism
Data Availability Statement
The original data presented in the study are openly available in Kaggle at https://www.kaggle.com/datasets/loki4514/rice-leaf-diseases-detection.
Funding
This work was partially supported by the National Science and Technology Council, Taiwan, R.O.C., under Grant NSTC 114-2221-E-390-007.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Chang, C. Y., & Lai, C. C. (2026). Classification of Rice Leaf Diseases Based on Lightweight Deep Learning Model. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 142-159. https://doi.org/10.62762/TETAI.2025.878660
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TY - JOUR AU - Chang, Chao-Yun AU - Lai, Chih-Chin PY - 2026 DA - 2026/05/26 TI - Classification of Rice Leaf Diseases Based on Lightweight Deep Learning Model JO - ICCK Transactions on Emerging Topics in Artificial Intelligence T2 - ICCK Transactions on Emerging Topics in Artificial Intelligence JF - ICCK Transactions on Emerging Topics in Artificial Intelligence VL - 3 IS - 2 SP - 142 EP - 159 DO - 10.62762/TETAI.2025.878660 UR - https://www.icck.org/article/abs/TETAI.2025.878660 KW - rice leaf disease classification KW - deep learning KW - lightweight convolutional neural network KW - attention mechanism AB - Traditional methods for classifying plant diseases usually depend on manual observation, which is time-consuming, labor-intensive, and prone to human error. The rise of deep learning has greatly advanced this field by enabling more accurate and efficient classification techniques. In this paper, we introduce a novel lightweight deep learning framework that builds on the RegNetY convolutional neural network architecture by incorporating a modified Efficient Channel Attention module. This enhancement is specifically designed to improve the classification of various rice leaf diseases. Our experiments on a publicly available dataset show that the proposed approach not only boosts classification accuracy but also significantly reduces computational complexity and memory usage, making it ideal for deployment on resource-limited edge devices. SN - 3068-6652 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Chang2026Classifica,
author = {Chao-Yun Chang and Chih-Chin Lai},
title = {Classification of Rice Leaf Diseases Based on Lightweight Deep Learning Model},
journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
year = {2026},
volume = {3},
number = {2},
pages = {142-159},
doi = {10.62762/TETAI.2025.878660},
url = {https://www.icck.org/article/abs/TETAI.2025.878660},
abstract = {Traditional methods for classifying plant diseases usually depend on manual observation, which is time-consuming, labor-intensive, and prone to human error. The rise of deep learning has greatly advanced this field by enabling more accurate and efficient classification techniques. In this paper, we introduce a novel lightweight deep learning framework that builds on the RegNetY convolutional neural network architecture by incorporating a modified Efficient Channel Attention module. This enhancement is specifically designed to improve the classification of various rice leaf diseases. Our experiments on a publicly available dataset show that the proposed approach not only boosts classification accuracy but also significantly reduces computational complexity and memory usage, making it ideal for deployment on resource-limited edge devices.},
keywords = {rice leaf disease classification, deep learning, lightweight convolutional neural network, attention mechanism},
issn = {3068-6652},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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