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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F21%3A00124254" target="_blank" >RIV/00216224:14740/21:00124254 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2077-0472/11/11/1098" target="_blank" >https://www.mdpi.com/2077-0472/11/11/1098</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/agriculture11111098" target="_blank" >10.3390/agriculture11111098</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Project

<a href="/en/project/EF16_026%2F0008446" target="_blank" >EF16_026/0008446: Signal integration and epigenetic reprograming for plant productivity</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

AGRICULTURE-BASEL

ISSN

2077-0472

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

1098

UT code for WoS article

000725852100001

EID of the result in the Scopus database

2-s2.0-85118852160