[2603.26761] Tiny-ViT: A Compact Vision Transformer for Efficient and Explainable Potato Leaf Disease Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.26761 (cs) [Submitted on 23 Mar 2026] Title:Tiny-ViT: A Compact Vision Transformer for Efficient and Explainable Potato Leaf Disease Classification Authors:Shakil Mia, Umme Habiba, Urmi Akter, SK Rezwana Quadir Raisa, Jeba Maliha, Md. Iqbal Hossain, Md. Shakhauat Hossan Sumon View a PDF of the paper titled Tiny-ViT: A Compact Vision Transformer for Efficient and Explainable Potato Leaf Disease Classification, by Shakil Mia and 6 other authors View PDF Abstract:Early and precise identification of plant diseases, especially in potato crops is important to ensure the health of the crops and ensure the maximum yield . Potato leaf diseases, such as Early Blight and Late Blight, pose significant challenges to farmers, often resulting in yield losses and increased pesticide use. Traditional methods of detection are not only time-consuming, but are also subject to human error, which is why automated and efficient methods are required. The paper introduces a new method of potato leaf disease classification Tiny-ViT model, which is a small and effective Vision Transformer (ViT) developed to be used in resource-limited systems. The model is tested on a dataset of three classes, namely Early Blight, Late Blight, and Healthy leaves, and the preprocessing procedures include resizing, CLAHE, and Gaussian blur to improve the quality of the image. Tiny-ViT model has an impressive test accuracy of 99.85% and a mean CV acc...