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In order to improve the quality of rice seed and eliminate weedy rice seeds, an adhesion segmentation algorithm based on concave point matching was proposed, and an online shape and color double choice rice seed recognition platform was built. The platform consisted of seed metering system, image acquisition system, transmission system and motor drive system. The algorithm of the platform was based on the concave point segmentation method of ECMM. Firstly, the collected image was preprocessed, and the adhesion contour with morphological factor less than 0.4 was extracted. The edge of the extracted contour was smoothed by one-dimensional Gaussian convolution kernel, and the curvature and mean curvature of the smooth contour curve were calculated. Several points that were different from the mean curvature were found as corners. Secondly, according to the positive and negative of the vector triangle area to determine whether the corner was a real concave point, the angle range (0°~180°) was found between the concave point and the normal direction composed of the preceding point and the successor point, and the matching concave point pairs in this angle range was found to complete the adhesion segmentation. The average accuracy of the algorithm was 92.90%, which was 19.82 percentage points higher than that of the limit corrosion method and 12.85 percentage points higher than that of the watershed algorithm. Finally, the length of seeds in each contour of the segmented image and the proportion of R channel pixels were calculated to identify weedy rice seeds. Through the identification platform test, the average time of 100 seeds per identification was 0.95s, and the average recognition accuracy was 97.50%.