Advanced AI Deep Learning Techniques for Detection and Classification of Plant Diseases Using Pre-Trained Models

Plant leaf diseases must be accurately and quickly detected in order to minimize financial losses and increase agricultural productivity. However, farmers' dependence on manual, conventional methods might occasionally result in incorrect diagnosis. In order to detect and identify plant leaf diseases, this study investigates the use of the VGG16 model, a form of Convolutional Neural Network (CNN). The extensive PlantVillage dataset, which includes more than 70,000 photos of both healthy and damaged leaves from 14 distinct crop species, is used in the study. Techniques for data augmentation, such as histogram equalization and horizontal flipping, were used to enrich the dataset and increase the model's flexibility. By contrasting the VGG16 model with other well-known CNN architectures including ResNet, Inception-V3, and VGG-19, its performance was carefully assessed. Utilizing the PlantVillage dataset, the suggested method produced an astounding 99.12% accuracy rate. The success of the suggested approach was confirmed by the exceptional performance of evaluation criteria including recall, precision, F1-score, or accuracy, all of which had values near 0.99. The results demonstrate VGG16's promise as a formidable instrument for precise plant disease diagnosis, providing notable breakthroughs in identifying plant diseases. Better crop safeguarding and disease management techniques are made possible by these advancements, which are extremely beneficial to all parties involved in farms, including farmers and researchers.