Tomato Disease Classification using Transformer-Based Deep Learning Models

Crop diseases present a major challenge to global agriculture, reducing yields and threatening food security. Traditional manual methods for detecting diseases in crops like tomatoes are labor-intensive and lack accuracy. This paper proposes a deep learning model based on the Transformer architecture for automated tomato leaf disease classification. Leveraging the self-attention mechanism of Transformers, the model captures global contextual features from leaf images, achieving superior recognition accuracy. The model was trained and tested on a diverse dataset comprising ten categories (nine diseases and healthy leaves), with data augmentation enhancing its robustness. Experimental results demonstrate that the Transformer-based model achieved a classification accuracy of $\mathbf{9 1. 7 7 \%}$, outperforming traditional CNN models such as VGG19 and ResNet50. This study highlights the potential of Transformer architectures in precision agriculture, enabling efficient and accurate disease recognition. Future work will focus on optimizing the model for deployment of mobile devices to facilitate real-time, on-field disease diagnosis.