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Strawberry is a healthy, beneficial fruit and one of the most valuable exports for most countries. However, diseases could produce poor-quality strawberries and affect the consumer's health. Thus, quality inspection is a crucial stage in processing production. Convolutional Neural Network (CNN) models can be used to identify specific diseases. Even yet, the performance of Vision Transformer (ViT) has recently improved by using transfer learning to detect strawberry diseases. The goal is to train this model to recognize those diseases, applying fine-tuning to increase the precision of the results to obtain high accuracy. Strawberry photos from the collection are divided into seven classes and mainly focus on strawberry leaves, berries, and flower diseases. The findings demonstrate the benefits of using the ViT model, which outperforms a similar approach to strawberry disease classification with accuracy and an F1-score of 0.927 and 0.927, respectively, on the Strawberry Disease Detection dataset. [ABSTRACT FROM AUTHOR]