Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods [preprint]

Preprint date

July 22, 2021

Authors

Henry J Nelson (Ph.D. student), Nikolaos Papanikolopoulos (professor)

Abstract

In order to apply the recent successes of automated plant phenotyping and machine learning on a large scale, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In this paper we notice a similarity between the current state-of-the-art for this problem and a commonly used density-based clustering algorithm, Quickshift. Exploiting this similarity we propose a number of novel, application specific algorithms with the goal of producing a general and scalable plant segmentation algorithm. The novel algorithms proposed in this work are shown to produce quantitatively better results than the current state-of-the-art while being less sensitive to input parameters and maintaining the same algorithmic time complexity. When incorporated into field-scale phenotyping systems, the proposed algorithms should work as a drop in replacement that can greatly improve the accuracy of results while ensuring that performance and scalability remain undiminished.

Link to full paper

Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods

Keywords

computer vision, precision agriculture