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

Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification