Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)

<b>Background</b>. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their tra...

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Autores principales:	Michael Henke, Kerstin Neumann, Thomas Altmann, Evgeny Gladilin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	plant image segmentation plant phenotyping ground truth data generation color spaces principle component analysis unsupervised data clustering Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/8645665951a24244b33fd01dc725232f
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spelling	oai:doaj.org-article:8645665951a24244b33fd01dc725232f2021-11-25T15:59:07ZSemi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)10.3390/agriculture111110982077-0472https://doaj.org/article/8645665951a24244b33fd01dc725232f2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1098https://doaj.org/toc/2077-0472<b>Background</b>. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. <b>Methods</b>. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). <b>Results</b>. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. <b>Conclusions</b>. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.Michael HenkeKerstin NeumannThomas AltmannEvgeny GladilinMDPI AGarticleplant image segmentationplant phenotypingground truth data generationcolor spacesprinciple component analysisunsupervised data clusteringAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1098, p 1098 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	plant image segmentation plant phenotyping ground truth data generation color spaces principle component analysis unsupervised data clustering Agriculture (General) S1-972
spellingShingle	plant image segmentation plant phenotyping ground truth data generation color spaces principle component analysis unsupervised data clustering Agriculture (General) S1-972 Michael Henke Kerstin Neumann Thomas Altmann Evgeny Gladilin Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
description	<b>Background</b>. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. <b>Methods</b>. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). <b>Results</b>. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. <b>Conclusions</b>. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.
format	article
author	Michael Henke Kerstin Neumann Thomas Altmann Evgeny Gladilin
author_facet	Michael Henke Kerstin Neumann Thomas Altmann Evgeny Gladilin
author_sort	Michael Henke
title	Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
title_short	Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
title_full	Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
title_fullStr	Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
title_full_unstemmed	Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
title_sort	semi-automated ground truth segmentation and phenotyping of plant structures using k-means clustering of eigen-colors (kmseg)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8645665951a24244b33fd01dc725232f
work_keys_str_mv	AT michaelhenke semiautomatedgroundtruthsegmentationandphenotypingofplantstructuresusingkmeansclusteringofeigencolorskmseg AT kerstinneumann semiautomatedgroundtruthsegmentationandphenotypingofplantstructuresusingkmeansclusteringofeigencolorskmseg AT thomasaltmann semiautomatedgroundtruthsegmentationandphenotypingofplantstructuresusingkmeansclusteringofeigencolorskmseg AT evgenygladilin semiautomatedgroundtruthsegmentationandphenotypingofplantstructuresusingkmeansclusteringofeigencolorskmseg
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Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)

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