Using machine learning to model different levels of salinity stress and silica fertilization of fenugreek (Trigonella foenum-graecum L.)

Fani, Ebrahim; Mokari, Mojtaba

doi:10.22034/nbr.11.2.57

Volume 11, Issue 2 (8-2024) nbr 2024, 11(2): 57-70 | Back to browse issues page

‎ 10.22034/nbr.11.2.57

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Fani E, Mokari M. Using machine learning to model different levels of salinity stress and silica fertilization of fenugreek (Trigonella foenum-graecum L.). nbr 2024; 11 (2) : 5
URL: http://nbr.khu.ac.ir/article-1-3671-en.html

Using machine learning to model different levels of salinity stress and silica fertilization of fenugreek (Trigonella foenum-graecum L.)

Ebrahim Fani ^*

, Mojtaba Mokari

Behbahan Khatam Alanbia University of Technology , fani@bkatu.ac.ir

Abstract: (1151 Views)

In recent years, the use of machine learning methods in various fields of agriculture is increasing, and these methods provide us with very good information for predicting and checking different levels of performance in plants. In the current research, according to the results of the preliminary experiment carried out previously with specific levels of salinity stress and fertilization (salinity stress levels of zero, 75 and 150 mM sodium chloride and fertilization levels of zero and 3 grams per liter of silica) which were previously carried out and using the nonlinear regression model (NLR) and Python programming language, the morphological and physiological traits of the fenugreek medicinal plant at the newly defined levels of salinity stress and silica fertilization (salinity of up to 300 mM level and silica fertilization in two levels of 1 and 2 grams per liter) were predicted without conducting practical tests and based on the levels of salinity and initial fertilization. The non-linear regression model is a widely used algorithm in data analysis where the relationship between variables is non-linear and can create meaningful relationships between variables using non-linear functions. The results showed that the positive effect of silica on the amount of chlorophyll fluorescence (Fv/Fm) can be seen from zero to 180 mM salinity level and the amount of greenness index (SPAD) from zero to 100 mM salinity level. It seems that according to the results of the present research, it is possible to use machine learning to investigate and analyze the morphological and physiological characteristics of the fenugreek medicinal plant at other defined levels of salinity stress and other defined silica fertilization with no need conduct a practical experiment.

Article number: 5

Keywords: Fenugreek, physiological characteristics, morphological characteristics, nonlinear regression method, machine learning

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