Enhancing multiclass plant disease classification using GAN-boosted vision transformer with XAI insights

Abstract

Introduction Agriculture is one of the major backbones of the Indian economy, where rice is the most prominent staple crop across the country. However, rice production has been significantly affected due to the occurrence of various plant diseases. Deep learning and machine learning have emerged as powerful solutions for computer vision-based problems. Methods This work identifies some of the key diseases and addresses these prominent ones using a state-of-the-art deep learning model. It proposes a novel multiclass rice leaf disease recognition model named GRG-ViT, which integrates Vision Transformer (ViT), Generative Artificial Intelligence (GenAI), and Explainable Artificial Intelligence (XAI) techniques for better outcomes. The Vision Transformer-based framework is designed to capture long-range spatial dependencies in leaf images, which enhances the model’s ability to identify the subtle disease patterns. Since the dataset portrayed considerable class imbalance, a GenAI-based synthetic data generation approach is equipped in this model to create balanced training samples, which in turn improves the model’s robustness. This model also proposes a hybrid Rectified Linear Unit (ReLU)–Gaussian Error Linear Unit (GELU)-based activation mechanism to attain effective feature representation. Results and discussion The obtained experimental results exhibit that the proposed GRG-ViT model reaches close to an overall accuracy of 96%, which outperforms conventional approaches. The incorporation of XAI methods like Gradient-weighted Class Activation Mapping (Grad-CAM) provides both interpretability and transparency by emphasizing the regions impacting the model’s actions. This research showcases the blended power of ViT, GenAI, and XAI in producing reliable and high-performing results for rice disease detection in precision agriculture.

Plant phenotyping relevance

イネ葉画像から病害状態を推定する画像解析モデルを開発し、データ拡張・分類性能・説明可能性を評価しており、病害フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe a GAN-boosted Vision Transformer for rice leaf disease classification using the Paddy Doctor dataset, but contain no authors' public code, trained model checkpoints, or paper-specific data deposit. The Paddy Doctor dataset is a third-party resource (IEEE DataPort) from prior work, not a per

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