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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/11/11/1098 |
| _version_ | 1827677746293112832 |
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| author | Michael Henke Kerstin Neumann Thomas Altmann Evgeny Gladilin |
| author_facet | Michael Henke Kerstin Neumann Thomas Altmann Evgeny Gladilin |
| author_sort | Michael Henke |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:48:12Z |
| format | Article |
| id | doaj.art-8645665951a24244b33fd01dc725232f |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T05:48:12Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-8645665951a24244b33fd01dc725232f2023-11-22T21:59:11ZengMDPI AGAgriculture2077-04722021-11-011111109810.3390/agriculture11111098Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)Michael Henke0Kerstin Neumann1Thomas Altmann2Evgeny Gladilin3Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, GermanyLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, GermanyLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, GermanyLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, Germany<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.https://www.mdpi.com/2077-0472/11/11/1098plant image segmentationplant phenotypingground truth data generationcolor spacesprinciple component analysisunsupervised data clustering |
| spellingShingle | 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) Agriculture plant image segmentation plant phenotyping ground truth data generation color spaces principle component analysis unsupervised data clustering |
| title | 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_short | 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 |
| topic | plant image segmentation plant phenotyping ground truth data generation color spaces principle component analysis unsupervised data clustering |
| url | https://www.mdpi.com/2077-0472/11/11/1098 |
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