LemnaTec Scanalyzers provide a comprehensive range of phenotyping research platforms from bench-top enclosures to systems that monitor large fields.
Hyperspectral Imaging of Plants
Hyperspectral imaging is a technology widely used for remote sensing in an effort to extract a maximum of information from images aquired under very instable imaging conditions, caused by the variability of the sunlight and atmospheric conditions. The key feature of these approaches is the use of hyprspectral indices (formulas to process wavelength intensities and ratios) as indicatoer for specific biologically relevant image content. for example chhlorophyll content, dried biomass, canopy density and more. On the other end of the spatial scale, Fourier Transform spectroscopy of biological material is used to assess specific ingredients contained in samples that are often grinded and dried, and thus show no spatial resolution at all. The application of hyperspectral imaging under highly controlled illumination conditions, as it is standard in all LemnaTec Scanalyzer3D imaging units, provides new options for data generation with high spectral resolution in a certain range of the full electromagnetic spectrum of frequencies. In contrast to the LemnaTec usage of multiple frequency imaging (VIS, NIR IR), hyperspectral imaging is rather focused on a smaller range (e.g. 400–1000 nm), but takes images at a spectral resolution between 1 and 10 nm. The user is thus enabled either to acquire full-spectrum datasets for each individual pixel of the image, or to restrict data acquisition to some specifically interesting frequency ranges. These will strongly depend on the substances, the substance groups or the general approach to hyperspectral data acquisition.
Approaches to hyperspectral imaging using LemnaTec hyperspec technologies
If the absorption and reflectance spectra of substances like chlorophyll, anthocyans or others are identified, hyperspectral images can achieve at least a semi-quantitative value for substance concentration. To obtain reliable data, a larger validation experiment, including measurement of the values with other methods, is necessary in order to develop a concentration model that relates spectral information to concentration.
Learned finger printing or pattern approach
When an individual substance would only be used as a surrogate value for a more complex physiological phenotype, it may often make sense not to replicate the chemical measurement by hyperspectral imaging (substance-specific approach), but to try accessing the physiological phenomenon more directly. Based on a larger set of full-spectral information and a combination of specific measurement methods or plant pre-treatments (or plants with known specific backgrounds), a set of spectra is generated with a correlation to specific plant conditions such as biotic or abiotic stressors, senescence, nutrient deficiencies or different stages of ripeness. By employing direct comparison of spectra, advanced statistical analysis or machine-learning processes, different patterns and the spectral regions of interest for the discrimination are identified. These areas will then be monitored under routine conditions to assess details of the plant status.
Pure pattern finding approach
To make the best of screening, which means to look out for the unknown, it is useful to assess a certain spectral resolution for the entire spectrum and the whole plant. After the experiment, specific algorithms search for patterns or deviations from control plants or similarities to known, interesting plant types in the experiment. This open approach minimises the need for extensive calibrations and retains the flexibility to detect the truly new and innovative traits.
- Plant Phenomics
- High Throughput Screening
- Climate Change
- Duckweed Growth Inhibition Test
- Field Phenotyping
- High Content Screening
- QTL Analysis
- Water Use Efficiency
- Abiotic Stress
- Plant Phenotyping, Plant Phenotype
- Controlled Environments
- Energy Crops
- Germplasm Characterisation
- Hyperspectral Imaging
- Root Development
- Smart breeding
- Drought Tolerance
- Environmental Simulation
- Growth Rate
- Non-destructive Plant Phenotyping
- Soil Water Content