关键词: 计算机视觉, 马铃薯分级, 品质检测

Abstract: The use of computer vision technology for potato postpartum quality grading has very important significance. First image analysis is carried out for the potato image obtained by computer vision system. The Otsu method is used to remove the background of the potato image, then the image processing and recognition is carried out for the potato states of damage, green skin and sprout. For the green skin potato, the Perceptron Learning Algorithm（PLA） is used to distinguish the normal potato and green peel potato. For the epidermis germinative potato, the edge detection method is used to get the image edge of potato in the area of each part. The K-Nearest Neighbor（KNN） classification algorithm is used to recognize the epidermis germinative potato, and the germination area profile is obtained by corner detection. Then for the edge detected, the median filter and the area of the solution are used to determine the potato skin injury, finally realizing the classification of potato quality. The potato quality test results：the recognition accuracy of normal potato is 96.8%, green peel potato is 89.7%, the skin injury potato is 90.4%, and the sprouting potato is 96%.

Key words: computer vision, potato classification, quality detection