GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection.

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Bibliographic Details
Title:	GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection.
Authors:	Islam, Saiful^1,2 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]
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Database:	Engineering Source

Description
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]
ISSN:	20888708
DOI:	10.11591/ijece.v16i3.pp1307-1318