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Accurate identification of orchard trunks can provide effective information for orchard robot localisation and navigation. The traditional tree trunk segmentation algorithm has low segmentation accuracy and poor real time performance. To solve this problem, a fast segmentation of tree trunks based on depth and texture features was proposed to improve segmentation accuracy and real time performance. Firstly, a Realsense depth camera was used to capture color and depth images of tree trunks. Then, a superpixel segmentation algorithm was proposed to segment color images, and fuse adjacent superpixel blocks with similar depth and texture values. Finally, plant trunks were distinguished from notrunk targets in candidate superpixel blocks based on trunk width threshold setting in depth images and hue value matching in color images. Both indoor and outdoor experiments were conducted to compare the proposed tree trunk segmentation algorithm with traditional GrabCut algorithm and K-means algorithm. The average recall rate and average accuracy of the new algorithm were 87.6% and 95.0%, respectively, while that of the GrabCut algorithm was only 78.0% and 92.8%, respectively, and the K-means algorithm was 80.2% and 89.1%, respectively. Meanwhile, the average time of the proposed algorithm was 0.20s, while the GrabCut algorithm was 0.66s, and the K-means algorithm was 4.42s. The experimental results showed that the proposed algorithm was effective in fast segmentation, and can be applied to tree trunk segmentation.