Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants
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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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- 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)
- PMC
- PMC4783663
- 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
- gold
- doi.org
- 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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