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

2026-03-23

AI Summary

This study addresses the inefficiency and error-proneness of conventional methods for identifying potato leaf diseases such as early and late blight. To overcome these limitations, the authors propose Tiny-ViT, a lightweight vision transformer model that integrates CLAHE and Gaussian blur preprocessing with GRAD-CAM for interpretability. Despite its minimal model size, Tiny-ViT achieves high classification accuracy, attaining 99.85% test accuracy, 99.82% mean cross-validation accuracy, and a Matthews correlation coefficient (MCC) of 0.9990 across three public datasets. The model demonstrates strong generalization capability, low computational overhead, and fast inference speed, making it well-suited for deployment on resource-constrained edge devices.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Search and Optimization: Learning to Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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 accuracy of 99.82% which is better than baseline models such as DEIT Small, SWIN Tiny, and MobileViT XS. In addition to this, the model has a Matthews Correlation Coefficient (MCC) of 0.9990 and narrow confidence intervals (CI) of [0.9980, 0.9995], which indicates high reliability and generalization. The training and testing inference time is competitive, and the model exhibits low computational expenses, thereby, making it applicable in real-time applications. Moreover, interpretability of the model is improved with the help of GRAD-CAM, which identifies diseased areas. Altogether, the proposed Tiny-ViT is a solution with a high level of robustness, efficiency, and explainability to the problem of plant disease classification.

Problem

• potato leaf disease
• disease classification
• efficient model
• explainable AI
• resource-constrained systems

Innovation

• Tiny-ViT
• Vision Transformer
• Explainable AI
• Plant Disease Classification
• Efficient Deep Learning