Proximal hyperspectral sensing in plant breeding

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 spatial resolution and data availability. The Tri-Spek is capable to cover a spectral a range from 400 – 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) above 0.8 (fresh matter 0.85; dry matter 0.90; leaf area index 0.90 and chlorophyll 0.84). Over three growing seasons performance tests with rye were applied at two test-sites in Germany Proceedings of the 13th International Conference on Precision Agriculture July 31 – August 3, 2016, St. Louis, Missouri, USA Page 2 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, heterogeneities within plant nurseries caused by elevation or soil differences can be identified indirectly by means of growth variations in the hyperspectral data. It can also be taken into account during the analysis of the breeding experiments, later.