This research explores the classification of grapevine leaf images using deep learning models, specifically VGG-16 and VGG-19. The study aims to leverage the capabilities of deep learning for agricultural applications, particularly in disease detection, plant health monitoring, and variety identification. A publicly available dataset consisting of 500 images (100 images per class) was used to train and evaluate the models. The VGG-16 model achieved an accuracy rate of 99.6%, while the VGG-19 model achieved a perfect accuracy rate of 100%. The VGG-19 model was found to be superior in classifying grapevine leaf images, demonstrating higher accuracy, precision, recall, and specificity compared to the VGG-16 model. The research highlights the potential of deep learning in agricultural applications and provides valuable insights for future studies in this domain.This research explores the classification of grapevine leaf images using deep learning models, specifically VGG-16 and VGG-19. The study aims to leverage the capabilities of deep learning for agricultural applications, particularly in disease detection, plant health monitoring, and variety identification. A publicly available dataset consisting of 500 images (100 images per class) was used to train and evaluate the models. The VGG-16 model achieved an accuracy rate of 99.6%, while the VGG-19 model achieved a perfect accuracy rate of 100%. The VGG-19 model was found to be superior in classifying grapevine leaf images, demonstrating higher accuracy, precision, recall, and specificity compared to the VGG-16 model. The research highlights the potential of deep learning in agricultural applications and provides valuable insights for future studies in this domain.