Abstract: 【Objective】 Because of the complexity of the natural environment, current tree contour extraction results are not satisfactory. This paper presents a method to extract a contour of multiple trees based on a Graph Cut algorithm to realize the boundary segmentation of multi-target trees in a single photo. 【Method】 First, this method enhances image details of each channel under RGB color space captured in the experiment by color histogram equalization. The graph of the s-t network is constructed using a Graph Cut algorithm to look for min-cut, and the image segmentation problem is transformed into the minimization of the energy function by marking the foreground and background pixels to achieve a single photo of many trees with preliminary image segmentation. Then, adaptive thresholding of gray-scale transformation is applied to the multiple-segmentation images to realize binarization of the images and morphological corrosion expansion, and the opening and closing operation processing of the binary images is used to achieve the filling, denoising, and smoothing of trees. On the basis of this morphological process, combined with the improved Canny operator edge detection technology, bilateral filtering is used instead of Gaussian filtering to enhance the boundary information to obtain a preliminary tree contour. Finally, according to the geometric position invariance of the photo’s trees, we use the geometric reconstruction method to express the features of target trees and judge their topological relationships. If there are topological relationship errors, we iterate the Graph Cut algorithm and geometry reassembly method again to obtain a better target tree extraction result. 【Result】 In order to validate the effectiveness of this method experimentally, we collected tree images in a natural environment. The results showed that this method can effectively separate the contour of every tree under different lighting conditions. The average error rate(A_f)was 5.62%, the false positive rate(R_FP)was 4.49%, and the false negative rate(R_FN)was 4.33%, which was better than those obtained by the traditional OTSU segmentation algorithm(41.40%, 26.73% and 10.99%, respectively), the K-means clustering algorithm(49.97%, 35.02% and 11.92%), and the C-V Plane Models(28.43%, 20.53% and 13.38% ). 【Conclusion】 In a complex natural environment, a Graph Cut algorithm based on human interaction can effectively separate vertical boundaries. The results provide a reference for the visualization and reconstruction of trees and feature extraction.