Intelligent Plant Disease Detection and Classification using HV-GNN with Ontology-Driven Knowledge Embedding

In recent years, intelligent plant disease detection has become an essential task in agriculture for ensuring crop health and improving yield. Detection and classification of plant diseases remain a challenging problem due to the vast numbers of plants species worldwide and the numerous diseases that negatively affect crop production. However, existing Deep Learning (DL) methods are used for plant disease detection and classification suffer from overfitting, limited robustness, and lack of interpretability. To overcome these issues, this paper proposes a Hybrid Vision Graph Neural Network with Ontology-Driven Knowledge Graph Embedding (HV-GNN-OKGE) method for effective plant disease detection and classification. The HV-GNN method captures both local and global structural features through isotropic and pyramid layers. While the ontology-driven embedding integrates semantic relationships among plant species, symptoms, and diseases to enrich classification with domain knowledge. The performance of the proposed HV-GNN-OKGE method is evaluated using two publicly available datasets: Plant Leaf Dataset (4,590 images, 22 classes) and PlantVillage (2,052 images, 2 classes) are used for training and evaluation, with preprocessing techniques including augmentation, resizing, and normalization to enhance image quality. Experimental results demonstrate that the proposed HV-GNN-OKGE achieves an accuracy of 99.95%, significantly outperforming existing deep learning models in plant disease detection and classification.