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Metro maps of plant disease dynamics--automated mining of differences using hyperspectral images.

Wahabzada M, Mahlein AK, Bauckhage C, Steiner U, Oerke EC, Kersting K - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.

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.

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Related in: MedlinePlus

Interpolated mean signatures and archetypal signatures for visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths (measured 4–14 dai).In the left column mean signatures of diseased barley plants before selecting disease archetypal signatures and in the right column mean archetypal signatures for η = 1 are illustrated. Archetypal signatures allow a better differentiation between different developing stages of the diseases. Moreover, they are in accordance to visually and manually extracted reflectance signatures during disease development. (Best viewed in color)
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pone.0116902.g003: Interpolated mean signatures and archetypal signatures for visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths (measured 4–14 dai).In the left column mean signatures of diseased barley plants before selecting disease archetypal signatures and in the right column mean archetypal signatures for η = 1 are illustrated. Archetypal signatures allow a better differentiation between different developing stages of the diseases. Moreover, they are in accordance to visually and manually extracted reflectance signatures during disease development. (Best viewed in color)

Mentions: To answer questions (MQ) and (SQ) we compared hyperspectral signatures before and after a selection of disease archetypal signatures. Automatically determined Dirichlet mean signatures per day are shown in Fig. 3 where the Dirichlet mean signatures in the left column were obtained prior to a selection of disease archetypal signatures, and the mean signatures in the right column were determined after selecting disease archetypal signatures. Disease archetypal signatures only consider pixels which are relevant and characteristic for a disease (Fig. 4), healthy pixels are neglected. Thus they constitute an accurate measure for a differentiation among diseases (Fig. 3, right column). Characteristic changes in the spectral reflectance are more distinct compared to regular mean signatures extracted from entire leaves (Fig. 3, left column). These archetypal signatures are additionally useful for distinguishing among different time points during pathogenesis which is discussed below. That is, each pathogen caused a characteristic disease progress and symptom appearance on barley plants and the following cold be deduced from archetypal signatures:


Metro maps of plant disease dynamics--automated mining of differences using hyperspectral images.

Wahabzada M, Mahlein AK, Bauckhage C, Steiner U, Oerke EC, Kersting K - PLoS ONE (2015)

Interpolated mean signatures and archetypal signatures for visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths (measured 4–14 dai).In the left column mean signatures of diseased barley plants before selecting disease archetypal signatures and in the right column mean archetypal signatures for η = 1 are illustrated. Archetypal signatures allow a better differentiation between different developing stages of the diseases. Moreover, they are in accordance to visually and manually extracted reflectance signatures during disease development. (Best viewed in color)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4306502&req=5

pone.0116902.g003: Interpolated mean signatures and archetypal signatures for visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelengths (measured 4–14 dai).In the left column mean signatures of diseased barley plants before selecting disease archetypal signatures and in the right column mean archetypal signatures for η = 1 are illustrated. Archetypal signatures allow a better differentiation between different developing stages of the diseases. Moreover, they are in accordance to visually and manually extracted reflectance signatures during disease development. (Best viewed in color)
Mentions: To answer questions (MQ) and (SQ) we compared hyperspectral signatures before and after a selection of disease archetypal signatures. Automatically determined Dirichlet mean signatures per day are shown in Fig. 3 where the Dirichlet mean signatures in the left column were obtained prior to a selection of disease archetypal signatures, and the mean signatures in the right column were determined after selecting disease archetypal signatures. Disease archetypal signatures only consider pixels which are relevant and characteristic for a disease (Fig. 4), healthy pixels are neglected. Thus they constitute an accurate measure for a differentiation among diseases (Fig. 3, right column). Characteristic changes in the spectral reflectance are more distinct compared to regular mean signatures extracted from entire leaves (Fig. 3, left column). These archetypal signatures are additionally useful for distinguishing among different time points during pathogenesis which is discussed below. That is, each pathogen caused a characteristic disease progress and symptom appearance on barley plants and the following cold be deduced from archetypal signatures:

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: INRES-Phytomedicine, University of Bonn, Bonn, Germany.

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.

Show MeSH
Related in: MedlinePlus