Other formats:
BibTeX
LaTeX
RIS
@article{1836435,
author = {Henke, Michael and Neumann, K. and Altmann, T. and Gladilin, E.},
article_location = {BASEL},
article_number = {11},
doi = {http://dx.doi.org/10.3390/agriculture11111098},
keywords = {plant image segmentation; plant phenotyping; ground truth data generation; color spaces; principle component analysis; unsupervised data clustering},
language = {eng},
issn = {2077-0472},
journal = {AGRICULTURE-BASEL},
title = {Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)},
url = {https://www.mdpi.com/2077-0472/11/11/1098},
volume = {11},
year = {2021}
}
TY - JOUR
ID - 1836435
AU - Henke, Michael - Neumann, K. - Altmann, T. - Gladilin, E.
PY - 2021
TI - Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
JF - AGRICULTURE-BASEL
VL - 11
IS - 11
SP - 1098
EP - 1098
PB - MDPI
SN - 20770472
KW - plant image segmentation
KW - plant phenotyping
KW - ground truth data generation
KW - color spaces
KW - principle component analysis
KW - unsupervised data clustering
UR - https://www.mdpi.com/2077-0472/11/11/1098
N2 - Background. 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. Methods. 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). Results. 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. Conclusions. 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.
ER -
HENKE, Michael, K. NEUMANN, T. ALTMANN and E. GLADILIN. Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg). \textit{AGRICULTURE-BASEL}. BASEL: MDPI, 2021, vol.~11, No~11, p.~1098-1110. ISSN~2077-0472. Available from: https://dx.doi.org/10.3390/agriculture11111098.
|
