  Plant Phenomics based on imaging based techniques can be used to monitor the
health and the diseases of plants and crops. The use of 3D data for plant
phenomics is a recent phenomenon. However, since 3D point cloud contains more
information than plant images, in this paper, we compare the performance of
different keypoint detectors and local feature descriptors combinations for the
plant growth stage and it's growth condition classification based on 3D point
clouds of the plants. We have also implemented a modified form of 3D SIFT
descriptor, that is invariant to rotation and is computationally less intense
than most of the 3D SIFT descriptors reported in the existing literature. The
performance is evaluated in terms of the classification accuracy and the
results are presented in terms of accuracy tables. We find the ISS-SHOT and the
SIFT-SIFT combinations consistently perform better and Fisher Vector (FV) is a
better encoder than Vector of Linearly Aggregated (VLAD) for such applications.
It can serve as a better modality.
