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| Hauptseite > Publikationsdatenbank > 3d imaging approaches in quantitative plant phenotyping: application scenarios in the lab and in the field |
| Hauptseite > Publikationsdatenbank > 3d imaging approaches in quantitative plant phenotyping: application scenarios in the lab and in the field |
| Conference Presentation (After Call) | FZJ-2017-00044 |
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2016
Abstract: In plant phenotyping 3-d imaging approaches are increasingly used to study the impact of genetic variability and environmental factors that influence leaf angles and light interception resulting in varying canopy architecture. The non-invasive acquisition of 3-d structure of different plant organs such as leaves, roots, fruits or seeds requires the employment of different methodological approaches. Apart from active methods, which make use of an artificial light source, e.g. Light Detection and Ranging (LIDAR), Light Sectioning or Structured Light, camera-based methods are widely used and substantially differ in terms of image data processing. Here we give an overview on 3-d imaging approaches, which have been developed at the Institute of Biosciences (IBG-2, Forschungszentrum Juelich, Germany) with a focus on the development and benchmarking of measurements as part of the two German project clusters Crop.Sense.net (www.cropsense.uni-bonn.de) and the German Plant Phenotyping Network (DPPN; www.dppn.de). We will demonstrate the use of different sensor techniques, ranging from Structured Light methods in the lab up to light sectioning approaches and stereo imaging in different applications scenarios in the lab and in the field thereby covering the scale of small plant populations to smaller scales of single plants. Using structured light we were able to resolve and quantitatively characterize single leaves up to a size of 2 mm. We will highlight the application of multi-camera setups under natural environmental conditions at the scale of experimental plots (up to 2 sqm) along with new image processing pipelines to estimate leaf area and leaf angle distribution in sugar beet experiments. Here plants were analyzed on the base of individual leaf 3-d models from segmented stereo images (Mueller-Linow et al., Plant Methods 2015).
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