<![CDATA[Rice is a fundamental food commodity globally, where accurate variety classification is crucial for pricing, quality control, and food security. Manual classification methods are labor-intensive, time-consuming, and prone to human subjectivity. This research proposes an automated classification system for five rice varieties (Arborio, Basmati, Ipsala, Jasmine, and Karacadag) using a Computer Vision approach with Vision Transformer (ViT) architecture. Unlike Convolutional Neural Networks (CNN) which focus on local features, ViT utilizes selfattention mechanisms to capture global contextual relationships within images. The model was developed using the public "Rice Image Dataset" containing 75,000 images. The methodology includes image preprocessing (resizing and normalization) and training of the ViT Classification model. Model performance was evaluated using standard metrics on a separate test set. The results show that the proposed ViT model achieved an outstanding accuracy of 99.9%. Thesefindings demonstrate that the Transformer-based approach is highly effective and efficient for automating rice variety identification,offering a more robust solution compared to conventional methods.]]>
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