Tracking of plant organs over spatio-temporal sequence of point cloud data is one of the demanding tasks of agricultural robotics for automated plant monitoring and growth analysis. Due to the complex geometry of plants, it is extremely difficult to identify and track the individual organs in different growth stages of plants. In this paper, we present an approach to perform correspondence estimation of different plant organs over a series of spatio-temporal data. The approach is based on two stages. In the first stage we develop a robust skeleton extraction method from unstructured plant point cloud data by adopting Hausdorff distance metric and modified breadth first search algorithm. The proposed skeletonization method is shown to be performing better than state-of-the-art, especially in handling very thin and delicate branches. We also address an overlooked problem of connecting skeleton points in the form of a graph, and demonstrate that different types of plant phenotype parameters can be obtained in a fully automatic manner from the skeleton graph. In the second stage, we exploit the skeleton graphs in developing an algorithm to perform correspondence estimation among the skeleton nodes using a cosine similarity based approach. We demonstrate the effectiveness of the proposed skeletonization technique in tracking different organs of the plant by finding good quality correspondences. Experiments are performed on three datasets on real and synthetic sequence of spatio-temporal plant point cloud data to demonstrate the effectiveness of the proposed method.