IGNN: An Attentional Graph-Based Approach for Vision Transformer-Empowered Plant Disease Detection

This study’s primary contribution is the development of a novel ViT-GNN hybrid deep learning model callled IGNN for plant disease classification. By combining the feature-learning prowess of a Vision Transformer with the relational-modeling capability of a Graph Neural Network, the model achieves superior performance and interpretability over traditional methods. This work demonstrates a new paradigm for leveraging both rich visual features and structural dataset relationships, advancing the field of automated plant disease diagnosis.