Segmentation of Bean-Plants Using Clustering Algorithms

Serkan Kartal, Sunita Choudhary, Michal Stočes, Pavel Šimek, Tomáš Vokoun and Vojtěch Novák

AGRIS on-line Papers in Economics and Informatics, 2020, vol. 12, issue 3

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.

Keywords: Agricultural and Food Policy; Research and Development/Tech Change/Emerging Technologies (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aolpei:320073

DOI: 10.22004/ag.econ.320073

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