Segmentation of Bean-Plants Using Clustering Algorithms

Abstract

In recent years laser scanning platforms have been proven to be a helpful tool for plants traits analysing in agricultural applications.Three-dimensional high throughput plant scanning platforms provide an opportunity to measure phenotypic traits which can be highly useful to plant breeders.But the measurement of phenotypic traits is still carried out with labor-intensive manual observations.Thanks to the computer vision techniques, these observations can be supported with effective and efficient plant phenotyping solutions.However, since the leaves and branches of some plant types overlap with other plants nearby after a certain period of time, it becomes challenging to obtain the phenotypical properties of a single plant.In this study, it is aimed to separate bean plants from each other by using common clustering algorithms and make them suitable for trait extractions.K-means, Hierarchical and Gaussian mixtures clustering algorithms were applied to segment overlapping beans.The experimental results show that K-means clustering is more robust and faster than the others.

Plant phenotyping relevance

豆類植物の重なりをクラスタリングで分離し、形質抽出に適したセグメンテーション手法を開発・比較しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's bean point-cloud dataset (ICRISAT/LeasyScan) and Python/scikit-learn clustering code are described but never deposited or given a public authors' URL. The phenospex.com link is the vendor platform page, not a paper-specific data or code asset, and the scikit-learn URLs are generic library documentation.

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