ArticleSensors (Basel, Switzerland)2023
A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 47 citations in OpenAlex.
- Tomato Leaf Color Diversity as a Functional Trait: Molecular Mechanisms, Physiological Significance, and Environmental Responses.International journal of molecular sciences · 2026Review
- Inverse-scattering of absorptive samples via beam propagation.bioRxiv : the preprint server for biology · 2026Article
- Synergistic mitigation of lead [Pb(II)] stress in Triticum aestivum L. by heavy metal-tolerant plant growth-promoting (HMT-PGP) microbes and N-acetylcysteine (NAC).Plant cell reports · 2026Article
- VIS-NIR-SWIR Hyperspectral Imaging and Advanced Machine and Deep Learning Algorithms for a Controlled Benchmark of Bean Seed Identification and Classification.Plants (Basel, Switzerland) · 2026Article
- Article
- Efficiency of Near-Infrared Spectroscopy in Quantifying Lignin in Black Liquor-Impregnated Reforestation Wood.Polymers · 2025Article
- In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology.Plant methods · 2025Article
- Developing a New Method of Transformation for Obtaining XYZ Color Values from RGB Images for Agricultural Applications.Sensors (Basel, Switzerland) · 2024Article
- Article
- Explainable Artificial Intelligence to Predict the Water Status of Cotton (Plants (Basel, Switzerland) · 2024Article
- Article
- Quantitative measurement of internal quality of carrots using hyperspectral imaging and multivariate analysis.Scientific reports · 2024Article
- Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis.Frontiers in plant science · 2024Article
- Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
- Simultaneous Quantification and Visualization of Photosynthetic Pigments inPlants (Basel, Switzerland) · 2023Article
- Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.Plants (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.