Comparison of VGG16 and VGG19 CNN Architecture for Rice Disease Image Classification

Abstract

The study aims to compare the performance of VGG16 and VGG19 architectures in classifying rice plant disease images using the Convolutional Neural Network (CNN) method. The models were trained using Adam and SGD optimizers with various learning rates, and evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. The results show that VGG19 with Adam's optimizer and learning rate 0.001 provides the best performance with a testing accuracy of 97.90% and the lowest validation loss value of 0.2910. VGG16 also showed good results with the highest validation accuracy of 89%, but the results were below VGG19. Based on the confusion matrix, both models accurately recognize some classes, although there are still errors in certain classes. Thus, the results obtained show that VGG19 is superior in terms of training accuracy and stability. The use of data augmentation techniques is expected to reduce the potential for overfitting.