09 HYPERRoot
Image gallery:
Contact:
BOKU, Institute of Agronomy.
Konrad Lorenz-Straße 24
3430 Tulln an der Donau
Email: gernot.bodner@boku.ac.at
Phone: +43 1 47654 95115
Web: https://boku.ac.at/en/dnw/pb
Key facts:
Description:
| Name | HYPERRoot |
|---|---|
| Location | BOKU, DNW, UFT. Konrad-Lorenz-Straße 24/II 3430 Tulln an der Donau |
| Category | Low throughput laboratory |
| Environment | Greenhouse grown plants in rhizoboxes |
| Sensors | Hyperspectral sensor (1000-1700 nm) |
| Traits | Root architecture, root and soil spectral properties, water content. |
| Capacity | 30 rhizoboxes available |
| Limitations |
– Manual loading of rhizoboxes into hyperspectral scanner. – Data processing not automatized; requires programming skills. |
| References |
Bodner, G., Nakhforoosh, A., Arnold, T., & Leitner, D. (2018). Hyperspectral imaging: a novel approach for plant root phenotyping. Plant methods, 14, 1-17. Bodner, G., Alsalem, M., Nakhforoosh, A., Arnold, T., & Leitner, D. (2017). RGB and spectral root imaging for plant phenotyping and physiological research: experimental setup and imaging protocols. JoVE (Journal of Visualized Experiments), (126), e56251. |
| URL | https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0352-1 |
HyperRoot provides hyperspectral images in the range between 1000 – 1700 nm with a 5 nm spectral resolution and a 0.1 mm spatial resolution. The rhizoboxes used for HyperRoot have a size of 1000 x 300 mm and thus allow for imaging mature plant root systems. The infrastructure is mainly intended for early phenotyping tasks related to root ecophysiology such as (i) investigating spectral features in root-soil systems encoding for biochemical characteristics (e.g., root senescence, water content of soil and root tissues), (ii) quantify water depletion around roots via water absorption bands, and (iii) spectral band detection for root-soil features for informing high-throughput multispectral root phenotyping pipelines. HyperRoot thus represents a scientific phenotyping infrastructure for hypothesis-driven root research and requires knowledge in processing hyperspectral imaging data.