title: Metro maps of plant disease dynamics--automated mining of differences using hyperspectral images.
abstract: Understanding the response dynamics of plants to biotic stress is essential to improve management practices and breeding strategies of crops and thus to proceed towards a more sustainable agriculture in the coming decades. In this context, hyperspectral imaging offers a particularly promising approach since it provides non-destructive measurements of plants correlated with internal structure and biochemical compounds. In this paper, we present a cascade of data mining techniques for fast and reliable data-driven sketching of complex hyperspectral dynamics in plant science and plant phenotyping. To achieve this, we build on top of a recent linear time matrix factorization technique, called Simplex Volume Maximization, in order to automatically discover archetypal hyperspectral signatures that are characteristic for particular diseases. The methods were applied on a data set of barley leaves (Hordeum vulgare) diseased with foliar plant pathogens Pyrenophora teres, Puccinia hordei and Blumeria graminis hordei. Towards more intuitive visualizations of plant disease dynamics, we use the archetypal signatures to create structured summaries that are inspired by metro maps, i.e. schematic diagrams of public transport networks. Metro maps of plant disease dynamics produced on several real-world data sets conform to plant physiological knowledge and explicitly illustrate the interaction between diseases and plants. Most importantly, they provide an abstract and interpretable view on plant disease progression.
authors.affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.
authors.forename: Mirwaes
authors.initials: M
authors.lastname: Wahabzada
authors.affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.
authors.forename: Anne-Katrin
authors.initials: AK
authors.lastname: Mahlein
authors.affiliation: Fraunhofer IAIS, Sankt Augustin, Germany; B-IT, University of Bonn, Bonn, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Bauckhage
authors.affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.
authors.forename: Ulrike
authors.initials: U
authors.lastname: Steiner
authors.affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.
authors.forename: Erich-Christian
authors.initials: EC
authors.lastname: Oerke
authors.affiliation: Fraunhofer IAIS, Sankt Augustin, Germany; CS Department, Technical University of Dortmund, Dortmund, Germany.
authors.forename: Kristian
authors.initials: K
authors.lastname: Kersting
mesh_terms: D001203:Ascomycota; D057225:Data Mining; D001467:Hordeum / Q000382:microbiology*; D054884:Host-Pathogen Interactions; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology*; D019265:Spectroscopy, Near-Infrared