B2-GraftingNet: A Hybrid Deep-Machine Learning Framework with Explainable AI for Automated Grape Leaf Disease Detection

Plant diseases increasingly threaten global agriculture due to climate change, yet manual diagnosis remains challenging. We introduce B2-GraftingNet, a lightweight deep-learning framework for automated grape-leaf disease detection that combines a VGG16 backbone with Inception-style blocks to learn robust multi-scale cues. Binary Particle Swarm Optimization selects the most informative features before classification. On the public Kaggle grape-leaf dataset, a cubic SVM classifier achieves 99.56% peak accuracy, surpassing standard pretrained CNNs (VGG16/VGG19: 34.04%, Xception: 97.95%, Darknet: 94.91%, ResNet-50: 98.44%) while being faster and lighter. For transparency, we incorporate Grad-CAM, LIME, and occlusion-sensitivity with a local 20B-parameter LLM via Ollama, which processes ROI-aware XAI summaries and provides plain-language guidance in structured sections (verdict, where to look, what to do next, caution) without suggesting specific treatments.