Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

<Previous Article In Volume
Next Article In Volume>

Enhanced Rice Disease Recognition Using Transfer Learning

Authors
Umme Habiba1, *, Rubaiya Islam Sadrin1, Ayesha Banu2, Md. Sabbir Hosen Mamun3, Riad Hossain4, Fatema-Tuj-Johora3
1Department of Computer Science and Engineering, Premier University, Chittagong, 4000, Bangladesh
2Department of Computer Science and Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, 4349, Bangladesh
3Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, 1216, Bangladesh
4School of Science, Engineering and Technology, East Delta University, Chittagong, 4209, Bangladesh
*Corresponding author. Email: ummekabir684@gmail.com
Corresponding Author
Umme Habiba
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_52How to use a DOI?
Keywords
transfer learning; deep learning; EfficientNetB3; MobileNetV2; precision agriculture
Abstract

Finding rice diseases is essential to maintaining the best possible crop health and reducing yield loss. By combining two different rice disease datasets and utilising transfer learning techniques, this study suggests a novel method for rice disease classification. Using the combined dataset, we optimised two pre-trained models, MobileNetV2 and EfficientNetB3. A confusion matrix and important performance metrics, such as accuracy, precision, recall, and F1-score, were used to assess the models. While the MobileNetV2 model achieved an 88% test accuracy, the EfficientNetB3 model achieved an impressive 91%. All disease classes had their precision, recall, and F1-scores calculated; the highest precision values of 1.00 were found for Tungro and Bacterial Blight. These findings offer important insights into enhancing agricultural management techniques and show how effective transfer learning models are at detecting rice diseases. The models’ classification performance was further improved by the merged dataset approach, which qualified them for practical use in precision agriculture.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_52How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Umme Habiba
AU  - Rubaiya Islam Sadrin
AU  - Ayesha Banu
AU  - Md. Sabbir Hosen Mamun
AU  - Riad Hossain
AU  - Fatema-Tuj-Johora
PY  - 2026
DA  - 2026/06/08
TI  - Enhanced Rice Disease Recognition Using Transfer Learning
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 755
EP  - 771
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_52
DO  - 10.2991/978-94-6239-664-7_52
ID  - Habiba2026
ER  -