ArticleSensors (Basel, Switzerland)2024
Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 29 citations in OpenAlex.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Analysis of trait differences and identification of superior early growth combinations in interspecific hybrid progenies of Gleditsia sinensis.BMC plant biology · 2026Article
- Multispectral Fluorescence Imaging for Fast Identification of Cold Stress in Pepper Plants.Sensors (Basel, Switzerland) · 2026Article
- The OJIP Kinetics Analysis Reveals Differential Thermal Tolerance Responses in Photosystem II ofPlants (Basel, Switzerland) · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Optical coherence tomography for early detection of crop infection.Plant methods · 2025Article
- A Method for Estimating Fluorescence Emission Spectra from the Image Data of Plant Grain and Leaves Without a Spectrometer.Journal of imaging · 2025Article
- A comprehensive review of crop stress detection: destructive, non-destructive, and ML-based approaches.Frontiers in plant science · 2025Review
- Exogenous application of serotonin, with the modulation of redox homeostasis and photosynthetic characteristics, enhances the drought resistance of the saffron plant.Scientific reports · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.
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Registered trials
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