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Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

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Abstract

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R 2 = 0.80−0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R 2 = 0.74 for four selected species, R 2 = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.

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Acknowledgements

The authors would like to thank Nicole Berger for maintaining the UAS as well as executing the flight campaigns, Daniel Schwab for providing his land as the experiment field and for seeding, raising and taking care of the crops, Eric Schweizer AG for the donations of cover crop seedlings, Fabienne Bauer, Matthias Botta and Dominique Flury for helping with field work and Paulette M. Kirkby and Elizabeth Steele for English editing.

Author Contributions

LR designed and maintained the experiment, designed and implemented the method and performed the analyses, figures and tables. The manuscript was drafted by LR with help and contributions from BS. Both authors read and approved the final manuscript.

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  1. Lukas Roth

    Present address: Institute of Agricultural Sciences, ETH Zurich, Universitätstr. 2, 8092, Zurich, Switzerland

Authors and Affiliations

  1. School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland

    Lukas Roth & Bernhard Streit

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  1. Lukas Roth
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Correspondence to Lukas Roth.

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Appendix

Appendix

See Fig. 9.

Fig. 9
figure 9

Scatter plot of biomass versus normalized differenced vegetation index (NDVI). a NDVI based on 700 nm band. b NDVI based on 670 nm band

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Roth, L., Streit, B. Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach. Precision Agric 19, 93–114 (2018). https://doi.org/10.1007/s11119-017-9501-1

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