GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection.
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| Title: | GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection. |
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| Authors: | Islam, Saiful1,2 saifulcse@iiuc.ac.bd, Akhtar, Md. Nasim2 drnasim@duet.ac.bd, Hassan, M. Mahadi2 mahadi_cse@yahoo.com, Karim, A. N. M. Rezaul2 zakianaser@yahoo.com, Habib, Israt Binteh2 israthabib.cse@gmail.com |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1307-1318. 12p. |
| Subjects: | Generative adversarial networks, Transformer models, Data augmentation, Edge computing, Rice diseases & pests, Agricultural technology, Deep learning |
| Abstract: | Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194285619 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Islam%2C+Saiful%22">Islam, Saiful</searchLink><relatesTo>1,2</relatesTo><i> saifulcse@iiuc.ac.bd</i><br /><searchLink fieldCode="AR" term="%22Akhtar%2C+Md%2E+Nasim%22">Akhtar, Md. Nasim</searchLink><relatesTo>2</relatesTo><i> drnasim@duet.ac.bd</i><br /><searchLink fieldCode="AR" term="%22Hassan%2C+M%2E+Mahadi%22">Hassan, M. Mahadi</searchLink><relatesTo>2</relatesTo><i> mahadi_cse@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Karim%2C+A%2E+N%2E+M%2E+Rezaul%22">Karim, A. N. M. Rezaul</searchLink><relatesTo>2</relatesTo><i> zakianaser@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Habib%2C+Israt+Binteh%22">Habib, Israt Binteh</searchLink><relatesTo>2</relatesTo><i> israthabib.cse@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1307-1318. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Rice+diseases+%26+pests%22">Rice diseases & pests</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+technology%22">Agricultural technology</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i3.pp1307-1318 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1307 Subjects: – SubjectFull: Generative adversarial networks Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Rice diseases & pests Type: general – SubjectFull: Agricultural technology Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Islam, Saiful – PersonEntity: Name: NameFull: Akhtar, Md. Nasim – PersonEntity: Name: NameFull: Hassan, M. Mahadi – PersonEntity: Name: NameFull: Karim, A. N. M. Rezaul – PersonEntity: Name: NameFull: Habib, Israt Binteh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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