Registration is a technique employed for the alignment of point clouds in a single coordinate system. This process is very useful for the reconstruction of 3D plant models, the extraction of their morphological features and the subsequent analysis of the phenotype. One of the most widely studied recording algorithms is ICP (Iterative Closest Point), which is based on rigid transformations. Although in the literature there are several comparative studies between different variants of ICP, there is no comparative study with other more recent existing methods based on other principles. Therefore, in this paper we present a study comparing the results obtained with different registration algorithms on previously filtered 3D point clouds of plants, obtained with a MS Kinect V1 sensor integrated to a rotating base. The study includes two of the most used variants of the ICP, the point-to-point ICP and the point-to-plane ICP. These variants are based on the normals to the surfaces found to guide their point-to-point matching method presenting better results in smooth regions. In addition, other iterative point cloud alignment algorithms based on probability density estimation, hierarchical mixed Gaussian models and distance minimization between probability distributions are included. The results showed the effectiveness of ICP variants simplicity, and the high precision achieved by probabilistic methods. The error and computation time of the algorithms, implemented in Python, were evaluated.