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Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants
Understanding the adaptation process of plants to (a)biotic stress is essential for improving management practices and breeding strategies of crops for sustainable agriculture in the coming decades. In this context, plant phenotyping is a main bottleneck in basic plant sciences and plant breeding, as it links genomics with complex responses of plants to varying environments. In particular, hyperspectral imaging is a promising approach for non-invasive, data-driven phenotyping, which allows for discovering non-destructively spectral characteristics of plants correlated with internal structure and physiological states in time-course experiments.
Unfortunately, data-driven phenotyping also presents unique computational problems in scale and interpretability: (1) Data is often gathered 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 who are not necessarily trained computer scientists. On the problem of mining hyperspectral images to uncover spectral characteristic and dynamics of stressed plants, I will showcase that both challenges can be met and that big data mining can—and should—play a key role for feeding a hungry world, while enriching and transforming data mining.
Based on joint work with Mirwaes Wahabzada, Anne-Katrin Mahlein, Ulrike Steiner, Erich-Christian Oerke, Christian Bauckhage, Christoph Römer, Lutz Plümer and many others summarised in:
Kristian Kersting, Christian Bauckhage, Mirwaes Wahabzada, Anne-Katrin Mahlein, Ulrike Steiner, Erich-Christian Oerke, Christoph Römer, Lutz Plümer:
Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. Computational Sustainability 2016: 99-120
Matheus Thomas Kuska, Anna Brugger, Stefan Thomas, Mirwaes Wahabzada, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, Anne-Katrin Mahlein: Spectral patterns reveal early resistance reactions of barley against Blumeria graminis f. sp. hordei. Phytopathology 107:1388-1398, 2017.
Mirwaes Wahabzada, Anne-Katrin Mahlein, Christian Bauckhage, Ulrike Steiner, Erich-Christian Oerke, Kristian Kersting: Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports (Nature) 6, 2016
Marlene Leucker, Mirwaes Wahabzada, Kristian Kersting, Madlaina Peter, Werner Beyer, Ulrike Steiner, Anne-Katrin Mahlein, Erich-Christian Oerke: Hyperspectral imaging reveals the effect of sugar beet QTLs on Cercospora leaf spot resistance. Functional Plant Biology 44:1-9, 2016
Mirwaes Wahabzada, Anne-Katrin Mahlein, Christian Bauckhage, Ulrike Steiner, Erich-Christian Oerke, Kristian Kersting: Metro maps of plant disease dynamics–-automated mining of differences using hyperspectral images. PLoS One 10(1):e0116902, 2015.
Matheus Kuska, Mirwaes Wahabzada, Marlene Leucker, Heinz-Wilhelm Dehne, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, Anne-Katrin Mahlein: Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods 11(1):28, 2015
Christian Thurau, Kristian Kersting, Mirwaes Wahabzada, Christian Bauckhage:
Descriptive matrix factorization for sustainability Adopting the principle of opposites. Data Min. Knowl. Discov. 24(2): 325-354 (2012)
Kristian Kersting is a Professor (W3) for Machine Learning at the CS Department of the TU Darmstadt, Germany, where he heads the machine learning lab. After receiving his Ph.D. from the University of Freiburg in 2006, he was with the MIT, Fraunhofer IAIS, the University of Bonn, and the TU Dortmund University. His main research interests are statistical relational AI, machine learning, and data mining, as well as their applications. Kristian has published over 150 peer-reviewed technical papers and co-authored a book on statistical relational AI. He received the European Association for Artificial Intelligence (EurAI, formerly ECCAI) Dissertation Award 2006 for the best AI dissertation in Europe as well as two best paper awards. He regularly serves on the PC (often at senior level) for several top conference and co-chaired ECML PKDD 2013 and UAI 2017.
For more information please visit http://www.ml.informatik.tu-darmstadt.de/