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Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants

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Computational Sustainability

Part of the book series: Studies in Computational Intelligence ((SCI,volume 645))

Abstract

Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture, ultimately helping to meet the challenge of “How to feed a hungry world?” Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Unfortunately, data-driven agriculture also presents unique computational problems in scale and interpretability: (1) Data is gathered often at massive scale, and (2) researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that yield easy-to-interpret insights for users that are not necessarily trained computer scientists. On the problem of mining hyperspectral images to uncover spectral characteristic and dynamics of drought stressed plants, we showcase that both challenges can be met and that big data mining can—and should—play a key role for feeding the world, while enriching and transforming data mining.

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Notes

  1. 1.

    In the long run, when plants are monitored over month, the online setting will be relevant.

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Acknowledgments

Parts of this work were supported by the Fraunhofer ATTRACT fellowship “Statistical Relational Activity Mining” and by the German Federal Ministry of Education and Research (BMBF) within the scope of the competitive grants program “Networks of excellence in agricultural and nutrition research - CROP.SENSe.net”, funding code: 0315529).

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Authors and Affiliations

  1. Computer Science Department, TU Dortmund University, Otto-Hahn-Strasse 14, 44221, Dortmund, Germany

    Kristian Kersting

  2. B-IT, University of Bonn, and Fraunhofer IAIS, Schloss Birlinghoven, 53757, Sankt Augustin, Germany

    Christian Bauckhage

  3. Institute for Crop Science and Resource Conservation (INRES)-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, 53115, Bonn, Germany

    Mirwaes Wahabzada, Anne-Kathrin Mahlein, Ulrike Steiner & Erich-Christian Oerke

  4. Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, Bonn, Germany

    Christoph Römer & Lutz Plümer

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  1. Kristian Kersting
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  3. Mirwaes Wahabzada
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  4. Anne-Kathrin Mahlein
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Correspondence to Kristian Kersting .

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Editors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Zittau/Gö, Goerlitz, Germany

    Jörg Lässig

  2. Fraunhofer IAIS, University of Bonn, Sankt Augustin, Germany

    Kristian Kersting

  3. Department of Computer Science, TU Dortmund University, Dortmund, Germany

    Katharina Morik

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Kersting, K. et al. (2016). Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-31858-5_6

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