3D reconstruction and visualization of plants are of great research value for their physiological characteristics. Large-scale datasets are required to train 3D reconstruction algorithms. Most of the current 3D plant datasets only focus on the parts above the ground surface. Roots, as the half below ground, are also critical for plant growth. There are few efforts to generate 3D root datasets. Existing 3D plant root datasets only contain a very limited number of reconstructed 3D models and the lack of ground truth, mainly because of the complexity of root structures and the inconvenience of obtaining root images and ground truth 3D structures. This paper creates a large virtual 3D plant root benchmark dataset, which not only contains extensive 3D root models; but also has 2D images captured from various angles, involving 1.2K 3D root structures and over 80K 2D root images. This large plant dataset enables testing of multiple 3D reconstruction algorithms to verify the reliability of the algorithms. Finally, multiple commonly used 3D reconstruction algorithms and the most recent state-of-the-art 3D modeling methods are tested on this dataset, which demonstrates the superb research values of this dataset to advance the 3D root modeling research.