Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis
Christoph Römer A F , Mirwaes Wahabzada B , Agim Ballvora C , Francisco Pinto D , Micol Rossini E , Cinzia Panigada E , Jan Behmann A , Jens Léon C , Christian Thurau B , Christian Bauckhage B , Kristian Kersting B , Uwe Rascher D and Lutz Plümer A
+ Author Affiliations
- Author Affiliations
A Institute of Geodesy and Geoinformation, Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn, Germany.
B Institute for Intelligent Analysis and Information Systems, Fraunhofer, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
C Institute of Crop Science and Resource Conservation, Plant Breeding and Biotechnology, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany.
D Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich, Leo-Brandt-Str., 52425 Jülich, Germany.
E Laboratorio di Telerilevamento delle Dinamiche Ambientali (LTDA), Dip. di Scienze dell’Ambiente e del Territorio (DISAT), Università degli Studi di Milano Bicocca (UNIMIB), Piazza della Scienza, 1, 20126 Milano, Italy.
F Corresponding author. Email: roemer@igg.uni-bonn.de
Functional Plant Biology 39(11) 878-890 https://doi.org/10.1071/FP12060
Submitted: 24 February 2012 Accepted: 8 July 2012 Published: 28 August 2012
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.
Additional keywords: canopy, imaging spectroscopy, matrix factorisation, non-invasive, pattern recognition, plant phenotyping, unsupervised learning, water stress.
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