Efficient Tomato Leaf Disease Classification Using Adapter Based Fine Tuning of Vision Transformers

The rapid expansion of deep learning in agricultural diagnostics has driven a shift toward lightweight architectures capable of operating on resource-constrained devices. While Convolutional Neural Networks (CNNs) have demonstrated success in plant disease classification, they struggle to capture global context and suffer from computational inefficiency when fine-tuning large-scale models on limited datasets. This study proposes a parameter-efficient fine-tuning (PEFT) approach using adapter modules integrated into Vision Transformer (ViT-Small-Patch16) architecture for automated tomato leaf disease classification across 10 disease classes. The method employs lightweight adapter modules comprising down-projection, ReLU activation, and up-projection layers with residual connections, inserted into each transformer block while freezing the pre-trained ViT backbone. This selective training strategy drastically reduces trainable parameters compared to full fine-tuning, mitigating overfitting risks on the PlantVillage dataset comprising 16,011 images. The model is trained using comprehensive data augmentation (HSV color adjustments, geometric transformations, and horizontal flips), stratified dataset partitioning (79.97% training, 10.03% validation, 10.00% test), and modern optimization techniques including AdamW optimizer, cosine annealing learning rate scheduler, label smoothing, gradient clipping, and early stopping. Experimental results demonstrate exceptional performance, achieving 99.81% accuracy, precision, recall, and F1-score of 0.9981, with perfect AUC-ROC and AUC-PR scores (1.0000) across all 10 disease classes. The model maintains computational efficiency with an inference time of 34.15 ms per image, making it suitable for real-time agricultural monitoring systems. Notably, minority classes such as Tomato Mosaic Virus (37 test samples) achieved perfect classification metrics, underscoring the model's robustness across imbalanced class distributions. Comparative analysis with recent tomato disease classification methods reveals that the proposed ViT-Adapter approach achieves competitive or superior performance in accuracy (99.81%), F1-score (99.81%), and inference speed (34.15 ms) while covering the broadest disease taxonomy (10 classes).