Automated Rice Analysis for Sugar Content and Dietary Recommendation

Accurate rice sugar content analysis and dietary advice are essential for establishing good eating practices and avoiding chronic illnesses. However, the traditional approaches for determining the sugar content of rice are time-consuming, labour-intensive, and subject to human error. We created an automated rice analysis system that uses machine learning algorithms to precisely assess the type of rice and hence detect the sugar content and give individualized dietary advice in order to solve these issues. With a sensitivity value of 0.9, the system demonstrates excellent accuracy and dependability and demonstrates its capacity to properly detect rice samples with high sugar content. These outstanding measures guarantee that the study findings are extremely reliable and offer important information for dietary recommendations. The system's accuracy is 0.95, which highlights its capacity to accurately categorize rice samples while taking both high and low sugar content into account. The trained machine learning model has a false negative rate of 0.041 which is extremely low, thus allowing the users to rely on the model's output to make informed dietary decisions.