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A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation

Author

Listed:
  • Jakob Geipel

    (Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, Stuttgart 70599, Germany)

  • Johanna Link

    (Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, Stuttgart 70599, Germany)

  • Jan A. Wirwahn

    (Institute for Geoinformatics, University of Münster, Heisenbergstr. 2, Münster 48149, Germany)

  • Wilhelm Claupein

    (Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, Stuttgart 70599, Germany)

Abstract

The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based on an industrial multi-sensor camera and a customizable image processing routine. The system was tested in a split fertilized N field trial at different growth stages in between the end of stem elongation and the end of anthesis. The acquired multispectral images were processed to normalized difference vegetation index (NDVI) and red-edge inflection point (REIP) orthoimages for an analysis with simple linear regression models. The best results for the estimation of above-ground biomass were achieved with the NDVI (R 2 = 0.72–0.85, RMSE = 12.3%–17.6%), whereas N content was estimated best with the REIP (R 2 = 0.58–0.89, RMSE = 7.6%–11.7%). Moreover, NDVI and REIP predicted grain yield at a high level of accuracy (R 2 = 0.89–0.94, RMSE = 9.0%–12.1%). Grain protein content could be predicted best with the REIP (R 2 = 0.76–0.86, RMSE = 3.6%–4.7%), with the limitation of prediction inaccuracies for N-deficient canopies.

Suggested Citation

  • Jakob Geipel & Johanna Link & Jan A. Wirwahn & Wilhelm Claupein, 2016. "A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation," Agriculture, MDPI, vol. 6(1), pages 1-19, January.
    • Handle: RePEc:gam:jagris:v:6:y:2016:i:1:p:4-:d:62363
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    References listed on IDEAS

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    1. Unknown, 2010. "Graduate School Agricultural Economics," 2010 Conference: Modern management challenges in the agro-food sector, March 18-19, Pivola, Slovenia 183903, Slovenian Association of Agricultural Economists (DAES).
    2. Franken, Jason R.V. & Cook, Michael L., 2015. "Investment Constraints in Agricultural Cooperatives," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205427, Agricultural and Applied Economics Association.
    3. Henehan, Brian M. & Li, Jie, 2010. "Organic Agriculture in New York State," EB Series 121649, Cornell University, Department of Applied Economics and Management.
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    Cited by:

    1. Christoph W. Zecha & Gerassimos G. Peteinatos & Johanna Link & Wilhelm Claupein, 2018. "Utilisation of Ground and Airborne Optical Sensors for Nitrogen Level Identification and Yield Prediction in Wheat," Agriculture, MDPI, vol. 8(6), pages 1-13, June.
    2. Niko Viljanen & Eija Honkavaara & Roope Näsi & Teemu Hakala & Oiva Niemeläinen & Jere Kaivosoja, 2018. "A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone," Agriculture, MDPI, vol. 8(5), pages 1-28, May.

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