Journal
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS
Volume -, Issue 126, Pages -Publisher
JOURNAL OF VISUALIZED EXPERIMENTS
DOI: 10.3791/56251
Keywords
Environmental Sciences; Issue 126; Root System; Hyperspectral Imaging; RGB Images; Plant stress; Phenotyping; Rhizobox; Spectral Analysis
Categories
Funding
- Austrian Science Fund FWF [P 25190-B16]
- Federal Government of Lower Austria [K3-F-282/001-2012]
- AGRANA Research & Innovation Center GmbH (ARIC)
- Austrian Science Fund (FWF) [P 25190] Funding Source: researchfish
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Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development overtime. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.
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