Dr. Ebrahim Fani, Dr. Mojtaba Mokari,
Volume 11, Issue 2 (8-2024)
Abstract
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.
Mina Rabie, Younes Asri, Fatemeh Sefidkon,
Volume 11, Issue 2 (8-2024)
Abstract
Abstract. Seseli olivieri (Apiaceae) is an exclusive species of the Alborz Mountains, Iran. In this research, the effect of environmental conditions on the vegetative traits and essential oil compounds of this species was investigated. For this purpose, three habitats with different heights were selected and the vegetative characteristics of this species were measured. In each habitat, soil samples and flowering branches of this species were collected and analyzed in the laboratory. The relationship between the functional traits of this species and environmental factors was determined using CA and PCA. Variance analysis of functional traits and soil parameters showed a significant difference between the three habitats. The highest values of vegetative traits were related to Tuyeh habitat. In Tange Kavard habitat, the main effective substances were Apiol and cis-Cadina-1(6),4-diene; in Enzo habitat, Apiol and Bornyl acetate; and in the Tuyeh habitat, Bornyl acetate and α-Pinene. Among the environmental factors, altitude, annual precipitation, annual temperature, minimum temperature of the coldest month, minimum absolute temperature, lime and nitrogen had the most significant correlation with the functional traits of this plant. Based on the IUCN criteria, the conservation status of this species was determined in the critically endangered.
