Explainable hybrid multi-branch CNN-ViT-GNN framework for robust hibiscus leaf disease classification

Early and reliable diagnosis of hibiscus leaf diseases is critical to protect horticultural yield. Yet, it remains challenging under real-time field conditions where uncontrolled lighting, clutter, and the non-contiguous nature of pathological symptoms blur diagnostic cues. To address these challenges, we introduce CNN-FusionViT-GNN. This explainable hybrid multi-branch framework synergizes the fine-grained texture extraction of a DenseNet201 backbone, the global contextual modeling of a Vision Transformer (ViT), and the relational reasoning of a Graph Neural Network (GNN). The model is trained and validated on 'Hibiscus,' a curated field dataset of 1165 images from Bangladesh, which is strategically augmented to 8000 samples for robust training following a strict train-validation-test split. The proposed framework achieves a state-of-the-art accuracy of 98.33% with a macro F1-score of 0.98. The framework's generalization is confirmed through high performance on external datasets: 98.78% accuracy on the 52-class Plant City dataset and 83.88% on the 10-class Tomato Leaf Disease dataset, while maintaining a rapid inference time of 10-45 ms. Furthermore, a multi-faceted Explainable AI (XAI) audit using LIME, Grad-CAM++, ViT Attention Maps, and Occlusion Sensitivity validates that the model's decisions are driven by biologically meaningful symptom patterns rather than background artifacts. This study establishes a computationally efficient, transparent, and robust pathway for automated disease diagnosis in precision agriculture.