MUMBAI, India, June 26 -- Intellectual Property India has published a patent application (202441100130 A) filed by Gm University on December 17, 2024, for System And Method For Automated Rice Grain Classification, Quality Grading, And Nutritional Analysis Using Machine Learning.
Inventors include Kavyshree P N; Shravani M R; Shravani Sajjan T S; Tejaswini S; and Vachana C.
The application for the patent was published on June 19, 2026, under issue no. 25/2026.
Abstract: Title : System and Method for Automated Rice Grain Classification, Quality Grading, and Nutritional Analysis Using Machine Learning The present invention relates to a system and method for automated classification and quality analysis of rice grains using machine learning techniques and image processing algorithms. The system incorporates an image processing module that pre-processes rice grain images by resizing them, normalizing pixel values, and extracting features using convolutional filters. A machine learning module, based on the VGG16 deep learning architecture, classifies rice grains into distinct categories based on various visual characteristics. The system includes a quality grading module that analyses the grains' dimensions and aspect ratios to assign them quality grades such as Slender, Medium, or Bold. Additionally, the system incorporates a nutritional analysis module that provides detailed nutritional data, including caloric content and macronutrient breakdown, for each rice type identified during the classification process. The results of the classification, quality grading, and nutritional analysis are presented to users through an intuitive web-based interface, enabling easy visualization and access to the data. Furthermore, the system is designed to detect and categorize broken rice grains, improving its functionality in real-world applications. This automated system aims to enhance the efficiency, accuracy, and consistency of rice grain classification and quality analysis, offering significant advantages in agricultural automation, quality control in rice processing plants, and consumer education regarding nutritional content.
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