Cited by 118Smart Agriculture and AIRemote Sensing in AgricultureSpectroscopy and Chemometric Analyses

Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

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Wahabzada, Mirwaes, Mahlein, Anne-Katrin, Bauckhage, Christian, Steiner, Ulrike, Oerke, Erich-Christian, Kersting, Kristian·2016·Scientific Reports

Abstract

Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we “wordify” the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.

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  • ML can help in disease detection, weed detection, and soil sensing.

    “ML can also help in disease detection, weed detection, and soil sensing [482,699,821].”

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  1. Jolliffe I. T. Principal Component Analysis, 1– 9 (Springer-Verlag: New York,, 2002) 2nd edn.
  2. Jensen J. R. Remote Sensing of the Environment–An Earth Resource Perspective 355– 408 (Pearson: Prentice Hall,, 2002) 2nd edn.

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Authors
Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., & Kersting, K.
Affiliations
University of Bonn (DE)
Corresponding author
Wahabzada, Mirwaes
Venue
Scientific Reports
Published
2016-03-09
Volume
6
Issue
1
Pages
22482-22482
ISSN
2045-2322 (ISSN-L)
OpenAlex
W2296030533
Cited by
118
References
51
Citations by year
2026: 6 · 2025: 7 · 2024: 4 · 2023: 7 · 2022: 15 · 2021: 15 · 2020: 14 · 2019: 20 · 2018: 13 · 2017: 12 · 2016: 5
FWCI
19.48
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, cc-by)
Funders
Bundesministerium für Bildung und Forschung (0315529, Funding code: 0315529)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Keywords
plant phenotyping, hyperspectral imaging, probabilistic topic models, plant disease detection, barley, foliar diseases, plant stress response, topic visualization, disease symptom development
MeSH terms
Hordeum, Phenotype, Plant Diseases, Stress, Physiological, Computational Biology, Optical Imaging

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