Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification
This work addresses plant phenomics for agricultural monitoring by evaluating 3D methods, but it is incremental as it compares existing techniques with a minor modification.
The paper compared various 3D keypoint detector and descriptor combinations for classifying plant health and growth stages using 3D point clouds, finding that ISS-SHOT and SIFT-SIFT performed best with Fisher Vector as a superior encoder, achieving specific classification accuracies as reported in tables.
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.