Sensors for Phenotyping

LemnaTec provides a wide range of approved sensors for all aspects of plant phenotyping. Sensor/cabinet combinations are tested in real research scenarios and the results are documented and supported by case studies and references.

Data from sensors can be translated into traits or biological properties using appropriate mathematical models. Such models establish links between non-invasive measurements and actual plant characteristics.

RGB Visible

Images captured in the visual spectrum are widely used to monitor plant development. There is a characteristic peak of reflectance around 550 nm, due to low absorbance of the pigments in this wavelength region[1]. This reflectance makes plants look green and enables us to detect plant tissue in RGB images.

Analysing RGB images delivers information about dimensions, morphologic and geometric properties, as well as colour distributions. By measuring the dimensions of visible plant tissue over time, we can monitor the growth of plants. Changes in growth are very sensitive indicators of plant stress. Morphology and colour distributions can serve as indicators of developmental processes and stress responses.

[1] Berger, Bettina; Regt, Bas de; Tester, Mark (2012): High-Throughput Phenotyping of Plant Shoots. In: Jennifer Normanly (editor.): High-Throughput Phenotyping in Plants, vol. 918. Totowa, NJ: Humana Press, pages 9–20. .

Laser Scanner

Laser scanners use active laser triangulation, or a phase shift method, to measure the complete morphological structure of plants in three dimensions[1]. The raw data is used to calculate a point cloud which provides detailed information about plant growth and movement. This process is faster than other image analysis techniques and point to point accuracy of 0.25mm is achievable. Near infrared scanners allow daylight scanning while minimising any interaction with plant physiology.

[1] Paulus, Stefan; Schumann, Henrik; Kuhlmann, Heiner; Léon, Jens (2014): High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. In: Biosystems Engineering 121, p. 1–11.


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[1].

Vegetation indices[2] 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.

[1] Govender, M.; Chetty, K.; Bulcock, H. (2007): A review of hyperspectral remote sensing and its application in vegetation and water resource studies. In: Water Sa 33 (2).

[2] Jackson, Ray D.; Huete, Alfredo R. (1991): Interpreting vegetation indices. In: Preventive Veterinary Medicine 11 (3), S. 185–200.

PS2 Fluorescence

Chlorophyll fluorescence analysis is used to measure photosynthetic parameters[1]. By exposing plants to a very bright flash of light, i.e. a saturating light pulse, the reactive centres of the plants’ photosystems become saturated and, using the Kautsky effect[2], the resulting chlorophyll fluorescence can be measured. Images from the PS2-cameras are processed by the LemnaTec-OS software which extracts meaningful physiological phenotyping data, in particular chlorophyll fluorescence parameters, such as the quantum yield.

[1] Baker, Neil R. (2008): Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo. In: Annual Review of Plant Biology 59 (1), p. 89–113.

[2] Kautsky, H.; Hirsch, A. (1931): Neue Versuche zur Kohlensäureassimilation. In: Naturwissenschaften 19 (48), S. 964.; and adaptation, S. 445–483.


While exposed to the sun’s radiation, plant surfaces increase in temperature[1]. Infrared sensors monitor the intensity of IR radiation emitted from the plant’s surface which can then be displayed in artificial colours. Actual temperature is then inferred by using measurements and transformations which take emissivity, object properties and geometry into account.

[1] Jones, H. G. and Rotenberg, E. 2001. Energy, Radiation and Temperature Regulation in Plants. eLS.

Near Infrared

Water content in plant tissue causes reflectance of electromagnetic radiation in near (700-1300 nm) and mid (1300-2500) infrared wavelength ranges[1]. The transition from visible light to near-infrared (NIR) radiation is characterised by a sharp increase of reflectance at leaf surfaces, a phenomenon called red edge.

NIR images show areas with high water content, and consequently low NIR reflectance, as dark, whereas dryer regions have high NIR reflectance and appear bright. Using image analysis techniques, such reflectance patterns can be translated into colour-coded maps describing relative water content of plant tissue.

[1]PENUELAS, J.; Filella, I.; Biel, C.; Serrano, L.; SAVÉ, R. (1993): The reflectance at the 950–970 nm region as an indicator of plant water status. In: International Journal of Remote Sensing 14 (10), p. 1887–1905.; Berger, Bettina; Regt, Bas de; Tester, Mark (2012): High-Throughput Phenotyping of Plant Shoots. In: Jennifer Normanly (editor.): High-Throughput Phenotyping in Plants, vol. 918. Totowa, NJ: Humana Press, pages 9–20.


Upon illumination, green plants emit red to far red fluorescence deriving from chlorophyll. Depending on pigment content or environmental circumstances, fluorescence in additional wavelength ranges, e.g. yellow, may occur. Fluorescence ratios can be used to monitor environmental changes and thus enable early stress and strain detection. Detecting pigment-specific fluorescence may require sample-adapted excitation light and filters. In some experimental situations, fluorescent biomarkers are recorded, using appropriate excitation light and filters, to evaluate spatial occurrence and amount of biomarker in the sample.

Hans-Georg Luigs

Senior Engineer - Sensor Specialist

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