Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping

Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated b...

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Main Authors: Stefan Paulus, Jan Dupuis, Sebastian Riedel, Heiner Kuhlmann
Format: Article
Language:English
Published: MDPI AG 2014-07-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/14/7/12670
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spelling doaj-17c47979aaca454d99f38000e76ace712020-11-24T22:12:57ZengMDPI AGSensors1424-82202014-07-01147126701268610.3390/s140712670s140712670Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput PhenotypingStefan Paulus0Jan Dupuis1Sebastian Riedel2Heiner Kuhlmann3Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53315 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53315 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53315 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53315 Bonn, GermanyDue to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated by applying a histogram-based classification algorithm. The leaf organs were represented by meshing algorithms, while the stem organs were parameterized by a least-squares cylinder approximation. We introduced surface feature histograms with an accuracy of 96% for the separation of the barley organs, leaf and stem. This enables growth monitoring in a time course for barley plants. Its reliability was demonstrated by a comparison with manually fitted parameters with a correlation R2 = 0:99 for the leaf area and R2 = 0:98 for the cumulated stem height. A proof of concept has been given for its applicability for the detection of water stress in barley, where the extension growth of an irrigated and a non-irrigated plant has been monitored.http://www.mdpi.com/1424-8220/14/7/12670laser scanningautomatic parameterizationbarleyautomatic classificationplant phenotyping
collection DOAJ
language English
format Article
sources DOAJ
author Stefan Paulus
Jan Dupuis
Sebastian Riedel
Heiner Kuhlmann
spellingShingle Stefan Paulus
Jan Dupuis
Sebastian Riedel
Heiner Kuhlmann
Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
Sensors
laser scanning
automatic parameterization
barley
automatic classification
plant phenotyping
author_facet Stefan Paulus
Jan Dupuis
Sebastian Riedel
Heiner Kuhlmann
author_sort Stefan Paulus
title Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
title_short Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
title_full Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
title_fullStr Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
title_full_unstemmed Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
title_sort automated analysis of barley organs using 3d laser scanning: an approach for high throughput phenotyping
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2014-07-01
description Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated by applying a histogram-based classification algorithm. The leaf organs were represented by meshing algorithms, while the stem organs were parameterized by a least-squares cylinder approximation. We introduced surface feature histograms with an accuracy of 96% for the separation of the barley organs, leaf and stem. This enables growth monitoring in a time course for barley plants. Its reliability was demonstrated by a comparison with manually fitted parameters with a correlation R2 = 0:99 for the leaf area and R2 = 0:98 for the cumulated stem height. A proof of concept has been given for its applicability for the detection of water stress in barley, where the extension growth of an irrigated and a non-irrigated plant has been monitored.
topic laser scanning
automatic parameterization
barley
automatic classification
plant phenotyping
url http://www.mdpi.com/1424-8220/14/7/12670
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