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In order to achieve high-efficiency and low-cost 3D modeling of rapeseed plant and online measurement of phenotypic parameters, a method for rape branch 3D reconstruction and pod identification was proposed by using RGB-D camera. A Kinect sensor was adopted to gather the color and depth images of rape branch from four angles, and then the 3D point cloud of the branch was gained and filtered. A rotation transformation was performed on the point cloud that needed to be registered, and the surface normal vector and curvature of the point cloud were calculated. Point pairs were constituted the points with similar curvature, which were used for the initial registration of the point cloud by using the nearest point iterative (ICP) algorithm based on KD-tree search. Employing the reference value came from the initial registration error, the corresponding point distance threshold of ICP algorithm was adjusted, and then the new point cloud obtained by the initial registration was precisely registered by using the initial registration operation. Combined with the proposed 3D reconstruction and the specific color image processing, an integrated point cloud of the rape branch without the main stem was produced, and then the single rape pod was identified with the Euclidean distance clustering algorithm. The experiment results showed that the proposed method had good robust and realtime, the 3D structure of single pod was clearly visible, the average distance error of the point cloud was less than 0.48mm, and the overall recognition rate of the pod was no less than 96.76%.