Comparative Analysis of Plant Disease Detection System

Plant diseases pose a critical threat to global agriculture, necessitating rapid and precise detection methods. In this study, we present an enhanced deep learning framework for plant disease detection that outperforms existing benchmarks by integrating refined preprocessing, advanced feature extraction, and optimized classification techniques. Our model achieves superior accuracy and generalization across diverse disease categories, as confirmed by standard evaluation metrics, while addressing limitations of previous approaches such as sensitivity to low-quality images and class imbalance. These improvements offer a robust tool for real-world agricultural monitoring with potential applications in precision farming and scalable crop health management.