Colored rice quality inspection system using machine vision

The machine vision-based quality evaluation of colored rice is an important component of achieving automatic rice production and processing. This study took red indica rice as the research object and developed a machine vision system for inspecting flawed rice kernels that are broken, chalky, damaged or spotted. Near infrared images of colored rice samples were collected by the machine vision system. A support vector machine (SVM) classifier, with the input of the invariant moment ellipse major axis, was applied to identify the broken rice kemels in the images. Then, the head rice images were obtained, and another SVM performed the gray-level segmentation. The segmented areas were doubly examined using the centroid distance constraint and the pixel search positioning method, which allowed the chalky areas to be accurately extracted. Finally, the damaged and the spotted areas on the rice kernels were detected by using edge detection and morphological methods. The experimental results show that the recognition accuracy for broken kernels, chalkiness, and damaged and spotted areas reached 99.3%, 96.3% and 93.6%, respectively. In addition, the average running time of the proposed method was 0.15 s, with four types of defects detected at one-time. Hence, it was concluded that the proposed method has significant potential to be applied for rapid and accurate colored rice quality detection and to provide technical support for the machine vision-based inspection of the automated grading of rice.