This study presents RiceVision, a cross-platform software system for real-time rice variety identification using deep learning–based image analysis. Unlike prior work that primarily focuses on classification accuracy, RiceVision emphasizes reproducibility, deployment, and usability in real-world agricultural environments. The system integrates a web-based platform and an offline-capable Android application within a unified architecture, ensuring consistent preprocessing and inference across platforms. Deep learning models are deployed using TensorFlow and TensorFlow Lite to support both online and on-device inference. The proposed hybrid framework combines convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures using a stacked ensemble strategy. Experimental evaluation on a 62-class rice variety dataset demonstrated strong classification performance, where the stacked ensemble achieved an average 5-fold validation accuracy of 98.64%, outperforming individual VGG16 (90.64%) and ViT-B/16 (91.28%) models. The system further demonstrated stable convergence behavior and low inter-fold variance, indicating robust generalization capability. A centralized model management mechanism enables version control and seamless updates across deployment platforms. Detailed model configurations, validation results, and explainability analyses are provided in the Supplementary Material. RiceVision highlights the potential of deployable AI systems for practical decision support in digital agriculture.