ArticleScientific reports2022
Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Rapid monitoring of drought and salinity stress responses in wheat via potential Raman-derived biomarkers and traditional biochemical indicators.Scientific reports · 2026Article
- Combined effect of salt stress and high light in plants: from basic statistical approach to machine learning methods.BMC plant biology · 2025Article
- Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging.Biomedical optics express · 2024Article
- Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Article
- Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning.Scientific reports · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.
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Registered trials
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