Advancing Plant Disease Detection: Harnessing Deep Learning and Machine Vision

This research explores the application of deep learning and machine vision in detecting plant leaf diseases in agricultural settings, specifically focusing on datasets from farm villages.By combining real farm village data with synthetic data generated by Generative Adversarial Networks (GANs), three advanced convolutional neural network (CNN) models VGG16, ResNet50, and InceptionNet V3 are utilized through transfer learning.Transfer learning enhances model performance by fine-tuning pre-trained networks.The study evaluates the models systematically using metrics such as accuracy, precision, recall, and F1 score.The findings demonstrate the effectiveness of the methodology, with ResNet50 achieving the highest performance at 83.23%.This research contributes to the advancement of precision agriculture, offering promising implications for sustainable farming practices and optimizing crop yields.