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Feeding the world with big data: Uncovering spectral characteristics and dynamics of stressed plants

 
: Kersting, K.; Bauckhage, C.; Wahabzada, M.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Römer, C.; Plümer, L.

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Lässig, J.:
Computational sustainability
Cham: Springer International Publishing, 2016 (Studies in computational intelligence 645)
ISBN: 978-3-319-31856-1 (Print)
ISBN: 978-3-319-31858-5 (Online)
S.99-120
Englisch
Aufsatz in Buch
Fraunhofer IAIS ()

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.

: http://publica.fraunhofer.de/dokumente/N-423024.html