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Close-up laser scanning of plants

Analyzing the spatial development of young plants or plant organs requires a highly resolved and accurate representation of the plant surface. Therefore, close-up laser scanning systems are used to generate point clouds describing the three dimensional shape of plants. 

 

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Figure 1: Perceptron ScanWorks-V5 laser line scanner measuring plants (left), 3D point cloud of a tomato plant (right)

Point clouds are analyzed by automated or semi-automated algorithms that process the raw data (thinning, smoothing, etc.), separate the plant organs (Surface Feature Histograms (SFH), Support Vector Machines (SVM), etc.) and derive phenotypic parameters like plant volume, plant height, leaf area and much more.

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Figure 2: Workflow of point cloud analysis. Organ segmentation and phenotpyic parameter estimation are automated algorithms [Paulus 2013, Paulus 2014].

Measuring multiple plants over a period of several days enables the derivation of growth curves and the comparison of different genotypes or the quantification of stressors like drought.

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Figure 3: Growth of a tomato plant (left) and a maize plant (right) measured over a time period of 31 days.

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Figure 4: Automated detection of drought stress of barley plants

Video:

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Publications:

  • Paulus, S. ; Dupuis, J. ; Riedel, S. ; Kuhlmann, H. (2014)  Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping, Sensors 2014, 14(7), S. 12670-12686, doi:10.3390/s140712670
  • Paulus, S. ; Dupuis, J. ; Mahlein, A.-K. ; Kuhlmann, H. (2013) Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping, BMC Bioinformatics, 14 (238), doi:10.1186/1471-2105-14-238
  • Paulus, S. ; Dupuis, J. ; Schumann, H. ; Kuhlmann, H. (2013) Automatische Parameterextraktion aus hochauflösenden Laserscans - ein wichtiger Schritt bei der Phänotypisierung, In: Bornimer Agrartechnische Berichte, Heft 81, 19. und 20. Workshop 6.-7. Mai 2013 Berlin, S. 241-249

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