Research on Appearance Quality Inspection Method of Rice Based on YOLOv8

To address the issues of time-consuming, laborious, and inaccurate traditional detection methods of rice appearance quality, a complete set of rice appearance quality inspection schemes is proposed in this study based on the YOLOv8 object detection model. A method of detecting the whole kernel rate by using the rice image area was proposed that is based on the YOLOv8 detection model. This method is more efficient and faster than the traditional method of measuring the whole kernel rate by the weight of rice. The detection of yellow grains quality based on the color threshold and chalkiness degree of rice based on the gray threshold was realized. Finally, comparative tests were conducted to verify the effectiveness and feasibility of the detection method. Random rice samples were tested by manual testing methods following national standards. The results show that the whole kernel rate is 59.80%, the roughness rate is 78.10%, the mold rate is 4.32%, the chalkiness degree is 11.00%, and the grade is second-class indica rice. The same rice sample was tested using the method of this study. The results show that the whole kernel rate of 59.10%, the roughness rate of 77.30%, the mold rate of 4.20%, the chalkiness degree of 12.00%, and the grade is second-class indica rice. The results are basically consistent with the manual detection method of the national standard. The rice appearance quality detection method proposed in this study provides a reference for achieving efficient, non-destructive, accurate detection and preferential grading of rice quality.