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

## TL;DR

This paper introduces GRG-ViT, a new AI model that improves rice disease classification using vision transformers, synthetic data, and explainable AI techniques.

## Contribution

The novel GRG-ViT model combines Vision Transformer, Generative AI, and XAI for enhanced and interpretable rice leaf disease classification.

## Key findings

- GRG-ViT achieves nearly 96% accuracy in classifying rice leaf diseases.
- The model uses synthetic data generation to address class imbalance and improve robustness.
- XAI techniques like Grad-CAM are used to provide interpretability and transparency in model decisions.

## Abstract

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.

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.

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.