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

Kristian Kersting; Christian Bauckhage; Mirwaes Wahabzada; Anne-Katrin Mahlein; Ulrike Steiner; Erich-Christian Oerke; Christoph Römer; Lutz Plümer
In: Jörg Lässig; Kristian Kersting; Katharina Morik (Hrsg.). Computational Sustainability. Pages 99-120, Studies in Computational Intelligence, Vol. 645, Springer, 2016.


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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