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Accuracy Analysis of a Multi-View Stereo Approach for Phenotyping of Tomato Plants at the Organ Level
- Source :
- Sensors (Basel, Switzerland), Sensors, Vol 15, Iss 5, Pp 9651-9665 (2015), Sensors, Volume 15, Issue 5, Pages 9651-9665
- Publication Year :
- 2015
- Publisher :
- MDPI AG, 2015.
-
Abstract
- Accessing a plant’s 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.
- Subjects :
- Convex hull
Engineering
Laser scanning
Reference data (financial markets)
Point cloud
lcsh:Chemical technology
Biochemistry
Article
Analytical Chemistry
plant phenotyping
Imaging, Three-Dimensional
Solanum lycopersicum
Image Processing, Computer-Assisted
Structure from motion
lcsh:TP1-1185
Computer vision
SfM and MVS photogrammetry
Electrical and Electronic Engineering
Instrumentation
Plant Stems
business.industry
organ-level parameterization
3D reconstruction
Automation
Atomic and Molecular Physics, and Optics
Plant Leaves
Phenotype
Photogrammetry
close-up laser scanning
Artificial intelligence
business
Algorithms
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....99b16c37040b2a2cd631b27f7fc2af9e
- Full Text :
- https://doi.org/10.3390/s150509651