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. Historically, rice variety identification has been conducted manually or through limited genetic testing, but recent technological advancements offer more efficient and accurate methods for authentication. Traditional methods of rice variety classification often involve visual inspection by experts or limited genetic testing. While these methods can be accurate, they are time-consuming, labor-intensive, and may not be suitable for large-scale supply chains. The primary challenge is to develop a system capable of accurately classifying rice varieties based on physical and genetic characteristics. This involves leveraging technology to differentiate between different types and strains of rice, which may look similar to the naked eye. Therefore the need of global rice market expands and supply chains become increasingly complex, there's a growing need for robust methods to authenticate rice varieties. Ensuring that the rice being distributed matches the claimed variety is essential for maintaining consumer confidence, preventing fraud, and upholding the integrity of the supply chain. The project, "Food Authentication: Rice Variety Classification for Supply Chain Integrity," 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.