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To achieve super rice seeding according to the numbers of seeds per bunch, it requires precise detect the seeding quantity per bunch in the potted seeding tray. The traditional detection method based on the area and average gray has low detection precision, which could not accurately identify the number of seeds per bunch and reduce adult seedling rate. There is a close relationship between the shape features of seeds in single connected region and the seeding quantity. In this article，a method base on the improved shape factor was presented to detect the seeding quantity per bunch in the potted seedling tray. Firstly，the RGB weighting method was used to gray the color image, the Otsu algorithm was used to binary image processing，morphological filtering algorithm was used to remove the image noise. Secondly, the small image of seeds per punch in potted seedling tray was extracted by the masked locationbased technology and the single connected region on the small image was detected. Thirdly，the shape features of each seed were extracted，such as the area and perimeter of single connected region and area of the minimum enclosing circumscribing convex polygon. Then，the improved shape factor was computed according to shape features of each seed. Lastly, the improved shape factor and the area of single connected region were used to classify seed connected regions into cavity (including impurities), one particle, two particles, three particles, or four particles and above. After adding up the particles of each bunch, the seedling tray seeding quantity can be obtained. The result showed that the detection accuracy of the number of seeds between zero particle and three particles in every single connected region was up to 95% and the detection accuracy of the number of seeds more than four particles in every single connected region was up to 90%. The detection accuracy of the number of seeds in every bunch was up to 93%. Each image was processed less than 0.518 seconds. It’s proved that the method of potted seedlings tray sowing quantity detection meets the requirement of automated rice sowing test line. The research result can provide reference for the follow-up work of reseed.