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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.
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.