Efficient classification and quality assessment of rice varieties are essential for market pricing, food safety, and consumer satisfaction in the global rice sector. Leveraging pre-trained ResNet architectures, Rice-ResNet significantly enhances feature extraction, ensuring accurate classification and quality detection of rice cultivars. This system, accessible in Python repositories, promises improved crop management and yield. Despite requiring real-world implementation, Rice-ResNet marks a significant advancement in rice classification, fostering enriched digital experiences.
