Salinity occurs due to the accumulation of water-soluble salts in the upper part of the soil 3. One of the significant reasons for this decrease is climate change, which affects the weather and plant growth via stressor factors such as salinity. This production decreased to 20 million tons in 2018. Turkey produced approximately 23 million tons of wheat in 2015. Wheat is a nutritious plant that meets 20–80% of the energy and protein needs of humans 2. Besides, it is one of the most widely cultivated and produced herbal products globally thanks to the high amount of protein and carbohydrates it contains 1.
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Wheat ( Triticum aestivum L.) is one of the cereal products that can quickly adapt to environmental conditions. 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. 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.
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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. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes.