In this study, a visual grading system of vegetable grafting machine was developed. The study described key technology of visual grading system of vegetable grafting machine. First, the contrasting experiment was conducted between acquired images under blue background light and natural light conditions, with the blue background light chosen as lighting source. The Visual C++ platform with open-source computer vision library (Open CV) was used for the image processing. Subsequently, maximum frequency of total number of 0-valued pixels was predicted and used to extract the measurements of scion and rootstock stem diameters. Finally, the developed integrated visual grading system was experimented with 100 scions and rootstock seedlings. The results showed that success rate of grading reached up to 98%. This shows that selection and grading of scion and rootstock could be fully automated with this developed visual grading system. Hence, this technology would be greatly helpful for improving the grading accuracy and efficiency.

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