For agricultural robots in orchard positioning and navigation, the environmental background is complex and the illumination intensity changes greatly. In order to solve the problems, a rapid identification method of tree trunks was proposed by using the features of color, depth, width and parallel edges based on RGB-D camera. Firstly, the color image and depth image of the orchard were obtained by using RealSense depth camera. Then, the color image was converted into HSV color space, and superpixel segmentation was performed on the S component in HSV, and then adjacent superpixel blocks with similar color characteristics and depth characteristics were combined. Secondly, trunk width feature detection was carried out on the depth image, and the object whose width confidence rate was greater than the threshold value was regarded as the object to be processed. Finally, the parallel edge detection of the processed object was conducted, the region of interest (ROI) window was selected in the edge area of the object to be processed for edge detection, and the possible straight edge of the trunk edge was searched, when the confidence rate of the object edge was greater than the set threshold, the processed object was recognized as trunk, otherwise it was non-trunk. Through the extraction of multiple features of tree trunks, the recognition rate of tree trunks under different environments was improved effectively. In order to evaluate the performance of tree trunk recognition algorithm under strong light, normal light and weak light, a mobile robot platform was used to test in orchard environment. The experimental results showed that the recognition rate of this algorithm was 92.38%, 91.35% and 89.86% and the average time of each image was 0.54s, 0.66s and 0.76s under strong light, normal light and weak light, respectively, which can stably realize the trunk recognition in orchard environment.