Application of Advance Deep Learning Models For Plant Disease Detection

The aim is to develop a system that employs Convolutional Neural Networks (CNNs) for the accurate classification of plant diseases after preprocessing the dataset to enhance image quality, remove noise, and normalize the data. Comparative analyses with existing methods highlight the CNN-based approach's advantages in precision, speed, and generalization capability. The system holds practical implications in agriculture, offering farmers and pathologists a reliable tool for early disease detection, aiding in timely disease management and reducing crop losses. Future work may extend the system to real-time disease monitoring and incorporate other deep learning techniques for enhanced performance. The CNN model's future improvement involves refining the architecture, optimizing parameters, and adopting advanced methodologies to address overfitting. Despite potential challenges with image variations, lighting conditions, and disease stages, future research can focus on enhancing the model's robustness. Comparisons with VGG16 and ResNet50 indicate that the CNN model outperforms, establishing it as the superior approach for plant disease classification.