Rice is a staple food for over half of the world's population. With numerous rice varieties available, ensuring the authenticity and quality of rice throughout the supply chain is crucial for consumer trust and food security. This research aims to utilize advanced computer vision techniques and machine learning algorithms to enhance the efficiency and accuracy of rice variety classification. By training models on extensive datasets of rice images and genetic information, this research endeavors to develop a system capable of autonomously and accurately identifying rice varieties. The integration of machine learning allows for the extraction of subtle visual and genetic features, enabling precise variety classification. This advancement holds great promise for maintaining the integrity of the rice supply chain, ensuring consumers receive the quality and variety of rice they expect.