ArticlePlant methods2019
Terahertz spectral imaging based quantitative determination of spatial distribution of plant leaf constituents.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 38 citations in OpenAlex.
- Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2025Article
- Recent Advances in THz Detection of Water.International journal of molecular sciences · 2023Review
- Comparison of Physical and System Factors Impacting Hydration Sensing in Leaves Using Terahertz Time-Domain and Quantum Cascade Laser Feedback Interferometry Imaging.Sensors (Basel, Switzerland) · 2023Article
- Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.Plant methods · 2022Article
- Classification of rice leaf blast severity using hyperspectral imaging.Scientific reports · 2022Article
- Specific Ionic Recognition Using Fig's Xylem/Phloem Vessel as a Novel and Applicable Device: Lab-on-a Xylem/Phloem.ACS omega · 2022Article
- Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography.Frontiers in plant science · 2020Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPlant leaves have heterogeneous structures composed of spatially variable distribution of liquid, solid, and gaseous matter. Such contents and distribution characteristics correlate with the leaf vigor and phylogenic traits. Recently, terahertz (THz) techniques have been proved to access leaf water content and spatial heterogeneity distribution information, but the solid matter content and gas network information were usually ignored, even though they also affect the THz dielectric function of the leaf.
resultsA particle swarm optimization algorithm is employed for a one-off quantitative assay of spatial variability distribution of the leaf compositions from THz data, based on an extended Landau-Lifshitz-Looyenga model, and experimentally verified using
conclusionsThis THz imaging method provides quantitative assay of the leaf constituent contents with the spatial distribution feature, which has the potential for applications in crop disease diagnosis and farmland cultivation management.
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Registered trials
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