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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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 |
work_keys_str_mv |
AT stefanpaulus automatedanalysisofbarleyorgansusing3dlaserscanninganapproachforhighthroughputphenotyping AT jandupuis automatedanalysisofbarleyorgansusing3dlaserscanninganapproachforhighthroughputphenotyping AT sebastianriedel automatedanalysisofbarleyorgansusing3dlaserscanninganapproachforhighthroughputphenotyping AT heinerkuhlmann automatedanalysisofbarleyorgansusing3dlaserscanninganapproachforhighthroughputphenotyping |
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