Comparative Evaluation of CNN and Transformer Architectures for Cauliflower Disease Classification on a Harmonized 15-Class Dataset: Effects of Multi-Resolution Inputs and Data Augmentation

Source abstract

Accurate and timely identification of cauliflower diseases is essential for reducing crop losses and supporting effective disease management. Although deep learning has shown strong performance in plant disease classification, comprehensive comparisons of convolutional neural network (CNN) and Transformer architectures across different image resolutions and augmentation settings remain limited for cauliflower disease recognition. This study presents a benchmark evaluation using a harmonized dataset of 5,265 original cauliflower images distributed across 15 classes. Following stratified partitioning, offline augmentation increased the training subset from 3,703 to 7,980 images, while the validation and test subsets each contained 781 images. Six CNN architectures (ResNet50, DenseNet121, MobileNetV3-Large, EfficientNetV2-S, Xception, and ConvNeXt-Tiny) and two Transformer-based models (ViT-Base-Patch16 and Twins-SVT-Base) were fine-tuned using ImageNet-pretrained weights. Six experimental configurations combined input resolutions of 224 × 224, 300 × 300, and 384 × 384 with and without additional online augmentation. Performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and Grad-CAM visualizations. Twins-SVT-Base achieved the highest observed test accuracy under the 384 × 384 configuration with additional online augmentation, reaching 98.21% accuracy and a weighted F1-score of 98.17%. ViT-Base-Patch16-384 achieved its highest performance without additional online augmentation, obtaining 97.82% accuracy and a macro F1-score of 97.95%. Performance varied across image resolutions, augmentation settings, and model architectures. Additional online augmentation was associated with the largest observed improvement for Twins-SVT-Base, whereas ViT-Base-Patch16-384 performed best without it. Overall, the findings highlight the potential of modern CNN and Transformer architectures for cauliflower disease classification and emphasize the importance of architecture-specific optimization of image resolution and augmentation strategies.