<![CDATA[Rice plant diseases can significantly reduce productivity and threaten food security, making early and accurate detection essential. Traditional manual inspection methods are slow, subjective, and difficult to scale for field conditions. This study aims to develop a rice disease classification system using computer vision and the low-code Roboflow platform with the Vision Transformer (ViT) architecture. The Rice Diseases Image Dataset, consisting of four classes—BrownSpot, Healthy, Hispa, and LeafBlast—underwent preprocessing, data augmentation, and splitting before being trained using the ViT model. The best-performing model (Version 6) achieved an accuracy of 92,2% on the test set. The results demonstrate that the proposed low-code workflow effectively streamlines the deployment pipeline of rice disease classification models, achieving competitive performance suitable for rapid prototyping in precision agriculture. These findings provide an initial foundation for the application of computer vision technologies in supporting precision agriculture practices.]]>
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