Artificial Intelligence-Aided Diagnosis in Agriculture: Plant Disease Classification with Vision Transformer and CNN Models

Abstract

This study comparatively evaluates the performance of Convolutional Neural Networks (CNN), Vision Transformer (ViT), and Swin Transformer architectures for diagnosing plant leaf diseases. The experimental framework was evaluated using model effectiveness, F1 scores, and confusion matrices on the PlantVillage dataset, which contains 55,448 images from 39 different classes. The results show that the Swin Transformer achieved the highest diagnostic performance with a 100% F1 score, outperforming both CNN and ViT models. These findings confirm the high potential of transformer-based architectures for automated disease detection in agriculture.