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

## TL;DR

This paper evaluates various 3D keypoint detectors and descriptors for plant health classification using 3D point clouds, introducing a modified 3D SIFT descriptor and analyzing their effectiveness.

## Contribution

It compares different 3D keypoint and descriptor combinations for plant classification and proposes a computationally efficient, rotation-invariant 3D SIFT descriptor.

## Key findings

- ISS-SHOT and SIFT-SIFT combinations perform best.
- Fisher Vector encoding outperforms VLAD.
- Modified 3D SIFT is less computationally intensive.

## Abstract

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.