Rice-ResNet: Rice classification and quality detection by transferred ResNet deep model

Abstract

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.