Hyperspectral cameras record a 2D projection of the measured object by scanning lines and measuring spectra of each scan-line. This delivers a data-cube consisting of a stack of images of the object’s reflectance throughout a range of wavelengths. Such data cubes are generally large and targeted data evaluation is necessary for phenotyping purposes .
Multispectral cameras record a selection of a series of wavelength bands, but do not record contineous spectra as hyperspectral cameras do. Thus, they collect less data, but they are faster and easier to operate than hyperspectral cameras.
Vegetation indices  are calculated from a number of wavelengths – typically two or three spectral bands – from the measured spectra. They characterise plant properties such as physiological or structural characteristics, pigment content or responses to biotic and abiotic stress. Furthermore, data mining and machine learning approaches can serve for extracting detailed data out of the data cubes.
Spectral imaging enables physiological phenotyping. With hyperspectral cameras, you not only gain access to a broad range of vegetation indices, but you are enabled to analyze spectral signatures of sample properties. Such signatures can indicate stress responses, pathogen defense, nutritional status, or biochemical features. Applications are not restricted to plants, but the technology is applicable to non-plant organisms, food samples, or event technical items.
In plant phenotyping, LemnaTec offers hyperspectral imaging in automated systems such as HyperAIxpert, PhenoAIxpert HT/HTC, or CanopyAIxpert. Moreover, we are developing a new product that brings hyperspectral imaging to laboratory scale, making the imaging independent of automation, and thereby opening the methodology to broader ranges of samples.