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In order to measure the efficiency parameters in the hulling process of buckwheat huller, an online measuring method based on machine vision to measure the efficiency parameters of buckwheat hulling was presented. The image of the fast sliding buckwheat grains was captured. N(B-R) gray transformation was performed on the captured image of buckwheat grains with a light blue background, then with Otsu algorithm the background was segmented and a binary image of buckwheat grains was generated. A distance image of buckwheat grains was generated by performing Euclidean distance transformation on the binary image, a skeleton image of buckwheat grains was generated by performing thinning operation on that binary image, and then the corresponding pixel points of distance image and skeleton image were multiplied and a distanceskeleton image was generated. Seed points were extracted by performing neighborhood maximum filtering algorithm on the distanceskeleton image, the distance images were marked with seed points, and the touching buckwheat grains were segmented with watershed segmentation algorithm. An interactive labeling method was used to label the unshelled buckwheat, whole buckwheat rice, broken buckwheat rice and wrongly segmented buckwheat grains, and then the labeled buckwheat grains were used to train a BP neural network. In the online experiment, the recognition rates of unshelled buckwheat, whole buckwheat rice and broken buckwheat rice were 99.7%, 97.2% and 92.6% respectively and it took 4.79s to process and recognize an 1824 pixels×1368 pixels image containing 897 seeds. The results showed that the rate of unbroken buckwheat rice can reflect the hulling efficiency of buckwheat huller and the running time met the need of online measurement.