Abstract: The diameter of the seedlings, the growing point coordinates and the seedling length are not only key parameters of grafting robot for judging whether it can successfully graft, but also the important basis for the robot to estimate motion parameters and spatial position of an object. With the aim to achieve feature (the coordinate of growth point of grafted seedling, the long and short axis radius of the seedlings of rootstocks' cotyledon, the long and short axis radius of the seedlings of scion' cotyledon, the length of the seedlings of rootstocks and the seedlings of scion) measurement of grafted seedling for vegetable grafting robot, an algorithm of integrated technology was proposed based on machine vision and image processing. The several grafting seedling properties can be obtained on-line. The flowchart of the algorithm was given. There were three mainly steps included in the algorithm. Firstly, the image was preprocessed to find the target area by adjusting the camera focal length, setting the relative position between the camera and the seedling. Then, the seedling color image obtained by camera in experiment was transformed into a monochromatic image. Secondly, the target area was segmented by discontinuous gray for building the range of interesting. A Gauss average method was used to extract mid-line for the x-coordinate of the growth point of grafting seedling. An order statistics filtering with a 5×5 median mask was used to reduce the random noise. In order to increase the dynamic range of the gray levels in the seedling image being processed, a contrast stretching transformation was obtained and its the minimum and maximum gray levels was respectively 0.45 and 0.65. Image complement and threshold segmented based on OTSU were acquired. Finally, the algorithm was made with morphological methods and logical calculation to obtain the image coordinates of the long and the short axis radius of the seedlings of rootstock and scion. A manual measurement method was carried before the method based vision and image processing was done. The result of grafted seedling diameter and length based on artificial measurement provided a data comparison basis for the image processing method. Then, the diameters and length of grafted seedling were measured based on the machine vision and image processing method, and some raw results were acquired. Experimental results of grafted gourd seedling image showed that this algorithm was feasible and effective. Compared with the method of manual measurement, the maximum error of seedling length was about 0.02 mm and the maximum error of seedling diameter was about 0.04 mm. The average error was no more than 0.0053 mm. One main reason of causing errors was that diameter of a given seedling varied in different parts, and the cross section of the seedling stem was not a perfect circle. The other main reason was that the seedling became short and bending because the seedling stem was quite soft when it was caught in the middle of the caliper. The research results showed that it took 0.31 s to process a single image, which met the requirements of the design (at the speed of 12 trees per minutes). The experiment verified the feasibility and effectiveness of the proposed algorithm. It provided a technological support for the optimum design and development of the robot for grafting. It also can meet the real-time requirements of grafting.