At present, most mangoes still need to be sorted and graded through manual identification of volume and quality, resulting in low efficiency and lack of data management. Machine vision is an effective means to improve the efficiency of mango grading, but traditional industrial cameras can only obtain two-dimensional projections. In response to this situation, this paper uses a 3D structured light system to obtain mango shape descriptors combined with three-dimensional depth information. Then 80 correction sets are used as samples, and the fisher judgment method is used for pose detection, and the non-linear support vector machine establishes the volume and mass prediction models in the “flat” and “upright” poses. Error analysis is performed on the prediction set. The results show that after adding depth information, the accuracy of pose detection can be increased to 100%, and the average error of volume quality can be reduced to less than 5%.