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In order to analyze the morphological structure of cherry tree leaves and provide a theoretical basis for the light distribution of cherry tree canopy and the shaping and pruning of cherry fruit trees,a method for reconstructing cherry tree leaves was proposed based on improved Harris corner detection and Nurbs curve. The obtained original cherry tree leaf images were preprocessed by median filtering method and classical edge detection algorithm to obtain leaf contours. An algorithm was proposed to reconstruct the contour, aiming at the problems that a closed surface cannot be formed when the contour was reconstructed by the traditional NUBRS curve principle, and the feature points interfere with each other. The algorithm first divided the feature points into left and right parts, the contours of the left and right sides were reconstructed respectively, and then the two were connected to obtain a complete contour. Secondly, the improved Harris corner detection method was used to extract corner points as feature points. Thirdly, by detecting the degree of similarity between the gray value of the center point of the window and the gray values of other pixels in the surrounding n neighborhood, the difference between the gray values was calculated to set a threshold, and the corner points were extracted according to the threshold range. Finally, a virtual leaf was constructed according to the virtual contour. Experimental analysis showed that the improved algorithm greatly reduced the amount of useless computation, and the average consumption time was reduced from 4.61s to 2.30s. Based on the improved algorithm, the edge shape of cherry tree leaves can be perfectly maintained, which provided technical support for the calculation of light distribution in the cherry tree canopy.