With the development of 3D acquisition devices, 3D modeling method emerges in endlessly. In recent years, 3D reconstruction based on real data is focused on scanning and TOF imaging method. The main drawback of the former is that needs extensive man-machine interaction. The latter can transform a wide range of scene depth information into plant 3D point cloud data in a short time. Most plant reconstruction method based on TOF technology is center on brance structure, but for herbaceous plant, leaves are not easy to operate. So a 3D model reconstruction algorithm was presented based on depth information segmentation and clustering for potted and elevated strawberry. Firstly, the algorithm took discrete depth information as a significant reference standard for object segmentation while taking the depth-dimensional image as a global reference standard to extract discrete point cloud through depth stepping. Then, the algorithm used clustering algorithm based on density to filter out random noise, jump-edge noise as well as background noise. By applying Harris algorithm between color image and the intensity image, a robust registration result was got and an index relationship between point cloud and pixel points was found. Ultimately, 3D strawberry canopy morphology with full color information was reconstructed after following all those steps. Experimental results showed that the algorithm had achieved good effects on segmentation clustering and coloring for potted and elevated strawberry leaves. The accuracy of single-leaf length was around 93% while A-B line accuracy was close to 97%, which indicated that the reconstructed model accuracy met the requirements of phenotypic parameters extraction. The 3D model can provide a new strategy for picking robot in spatial structure research domain.