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

Michael Henke, Kerstin Neumann, Thomas Altmann and Evgeny Gladilin
Additional contact information
Michael Henke: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, Germany
Kerstin Neumann: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, Germany
Thomas Altmann: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, Germany
Evgeny Gladilin: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland, Germany

Agriculture, 2021, vol. 11, issue 11, 1-13

Abstract: 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.

Keywords: plant image segmentation; plant phenotyping; ground truth data generation; color spaces; principle component analysis; unsupervised data clustering (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/11/11/1098/pdf (application/pdf)
https://www.mdpi.com/2077-0472/11/11/1098/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:11:y:2021:i:11:p:1098-:d:672282

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().