Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms

The framework achieved exceptional performance on a dataset of 5,000 images across five classes (four diseases, one healthy). Feature fusion from dual streams and HGA-PSO optimization reduced dimensionality by 50-70% while preserving discriminative power. Under rigorous 5-fold cross-validation, the Multi-Layer Perceptron (MLP) classifier attained 99.10% accuracy, a perfect ROC-AUC score (1.00), and high precision-recall metrics. Confusion matrices revealed near-zero misclassification, and real-world tests (single/batch images) confirmed strong generalization. Grad-CAM + + visualizations validated precise localization of disease regions. The model outperformed existing techniques (e.g., PSO-YOLOv8: 98.86%, Transformer models: 93.13%) in accuracy, precision, recall, and F1-score CONCLUSIONS: This research presents an optimized model for pomegranate disease detection by combining deep learning with nature inspired optimization. The dual-stream feature fusion and HGA-PSO significantly improves robustness again environment variability while reducing computation overhead. This framework offers a scalable solution for precision agriculture, enabling early disease intervention to mitigate economic losses. Future research could improve scalability and usefulness by looking into lightweight optimization methods, model interpretability, and how they can be used in limited-resource agricultural settings.