Abstract: Accurate and efficient diagnostic methods are essential for crop health monitoring due to the substantial impact of tomato leaf diseases on crop yield and quality. Traditional machine learning models, such as convolutional neural networks (CNNs), have shown promise in plant disease classification; however, they often require extensive data preprocessing and struggle with complex variations in leaf appearance. This study introduces TLDViT (Tomato Leaf Disease Vision Transformer), a Vision Transformer model specifically designed for the classification of tomato leaf diseases. TLDViT reduces the need for preprocessing by learning disease-specific features directly from raw images, leveraging Vision Transformers’ ability to capture long-range dependencies within images. We evaluated TLDViT on the Plant Village Dataset, which includes healthy and diseased samples across multiple classes. For comparative analysis, two Vision Transformer models, ViT-r50-l32 and ViT-l16-fe, were tested. Among these, ViT-r50-l32 achieved the highest performance, surpassing both ViT-l16-fe with an accuracy of 98%. These findings highlight TLDViT’s potential as an effective tool for crop health monitoring and automated plant disease diagnosis.

Sami Aziz Alshammari, “TLDViT: A Vision Transformer Model for Tomato Leaf Disease Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151285

@article{Alshammari2024,
title = {TLDViT: A Vision Transformer Model for Tomato Leaf Disease Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151285},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151285},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Sami Aziz Alshammari}
}