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Early drought stress detection in cereals: Simplex volume maximization for hyperspectral image analysis

 
: Römer, Christoph; Wahabzada, Mirwaes; Ballvora, Agim; Pinto, Francisco; Rossini, Micol; Cinzia, Panigada; Behmann, Jan; Léon, Jens; Thurau, Christian; Bauckhage, Christian; Kersting, Kristian; Rascher, Uwe; Plümer, Lutz

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Preprint urn:nbn:de:0011-n-2249759 (758 KByte PDF)
MD5 Fingerprint: daaf766504ebcbe4b8a528d3e6817e4f
Erstellt am: 22.3.2013


Functional plant biology : FPB 39 (2012), Nr.11, S.878-890
ISSN: 1445-4408
ISSN: 0310-7841
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()
canopy; imaging spectroscopy; matrix factorisation; noninvasive; pattern recognition; plant phenotyping; unsupervised learning; water stress

Abstract
Early water stress recognition is of great relevance in precision plant breeding and production. Hyperspectral imaging sensors can be a valuable tool for early stress detection with high spatio-temporal resolution. They gather large, high dimensional data cubes posing a significant challenge to data analysis. Classical supervised learning algorithms often fail in applied plant sciences due to their need of labelled datasets, which are difficult to obtain. Therefore, new approaches for unsupervised learning of relevant patterns are needed. We apply for the first time a recent matrix factorisation technique, simplex volume maximisation (SiVM), to hyperspectral data. It is an unsupervised classification approach, optimised for fast computation of massive datasets. It allows calculation of how similar each spectrum is to observed typical spectra. This provides the means to express how likely it is that one plant is suffering from stress. The method was tested for drought stress, applied to potted barley plants in a controlled rain-out shelter experiment and to agricultural corn plots subjected to a two factorial field setup altering water and nutrient availability. Both experiments were conducted on the canopy level. SiVM was significantly better than using a combination of established vegetation indices. In the corn plots, SiVM clearly separated the different treatments, even though the effects on leaf and canopy traits were subtle.

: http://publica.fraunhofer.de/dokumente/N-224975.html