The use of remote sensing in plant breeding is challenging due to the large number of small parcels which at least actually cannot be measured with conventional techniques like air- or spaceborne sensors. On the one hand crop monitoring needs to be performed frequently, which demands reliable data availability. On the other hand hyperspectral remote sensing offers new methods for the detection of vegetation parameters in crop production, especially since methods for safe and efficient detection of phenotypic differences are essential to develop adapted varieties by breeding.

To address both aspects, a ground-based hyperspectral system called “TriSpek” has been developed to deploy new spectral opportunities and to overcome the problems of data availability and spatial resolution.

The TriSpek is capable to cover a spectral an effective spectral range from 400 to 825 nm with 1 nm bandwidth. Using multiple spectrometers allows for correcting the reflectance measurements for incoming radiation on the fly in the field. This option increases data availability since the effects of illumination situations due to different sun angles and clouds can be compensated directly in the field.

In an extensive calibration process partial least squares regression models for the determination of several vegetation parameters in rye have been developed. The results show a high prediction quality with coefficients of determination (R²) of 0.85 for  fresh matter, 0.90 for dry matter, 0.90 for leaf area index and 0.84 for chlorophyll-a, respectively.

Over three growing seasons performance tests with rye were applied at two test-sites in Germany with different candidate strains under drought stress and irrigation. Connecting the spectral/vegetation data to the digital field plans of the experiments allow views of the temporal and spatial dynamics. Applying this concept, heterogenities within plant nurseries caused by elevation or soil differences can be identified indirectly by means of growth variations in the hyperspectral data.