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

Area of Science:

• Agricultural Science
• Computer Science
• Artificial Intelligence

Background:

• Rice production is vital to India's economy but threatened by plant diseases.
• Computer vision, deep learning, and machine learning offer solutions for disease identification.

Purpose of the Study:

• To develop a novel multiclass rice leaf disease recognition model.
• To enhance disease detection accuracy and robustness in precision agriculture.

Main Methods:

• Proposed the GRG-ViT model, integrating Vision Transformer (ViT), Generative AI (GenAI), and Explainable AI (XAI).
• Utilized ViT for spatial dependency capture, GenAI for class imbalance mitigation, and a hybrid activation mechanism.
• Incorporated XAI methods like Gradient-weighted Class Activation Mapping (Grad-CAM) for interpretability.

Main Results:

• The GRG-ViT model achieved an overall accuracy of approximately 96%, outperforming conventional methods.
• GenAI effectively balanced the dataset, improving model robustness.
• XAI provided transparency by highlighting critical image regions for disease identification.

Conclusions:

• The GRG-ViT model demonstrates high performance and reliability for rice disease detection.
• The integration of ViT, GenAI, and XAI offers a powerful framework for precision agriculture.
• This research contributes to safeguarding rice production through advanced AI techniques.