The broken rice rate (BR) is a critical metric which influences the appearance, processing, and economic value of rice. However, current machine vision and machine learning approaches engender significant errors when calculating BR. This study introduces a novel restoring method for identifying BR by leveraging grading and morphological features. A three-class classification model using Convolutional Neural Network (CNN) was devised to distinguish broken rice types of crescent head, elliptical tail, and quadrilateral midst based on their morpho...
