Search published articles


Showing 2 results for Physiological Characteristics

Elmira Shokoohi, Omid Sofalian, Ali Asghari, Saeid Khomari, Behrooz Esmaielpour, Hamed Aflatooni,
Volume 10, Issue 2 (9-2023)
Abstract

Chickpea is one of the most important plants of the legume family and is very important in the diet. In order to investigate the genetic diversity of chickpea, an experiment was conducted with 18 chickpea genotypes in the form of a completely to investigate the genetic diversity of chickpea, an experiment was conducted with 18 chickpea genotypes in the form of a completely randomized block design. After acclimatization of plants to cold, freezing treatment was applied at temperatures of -6, -8 and -10 and their 50% lethality temperature (LT50) was determined by probit transformation. Before and after the habituation stage, a leaf sample was taken and the relative content of leaf water, photosynthetic pigments, proline, soluble sugar, protein percentage, catalase, peroxidase, polyphenol oxidase and greenness index were measured. Genotype number 5 with the lowest LT50 (-8.86) and the highest survival percentage (80%) was the most resistant genotype and genotype 10 with the highest LT50 (-3.57) and the lowest survival percentage along with genotype 15 were recognized as the most sensitive genotypes. In order to evaluate genetic diversity, DNA extraction was utilized and 21 different ISSR primers were used in the investigation. The results showed the presence of polymorphism among the cultivars studied. A total of 101 clear bands were produced, of which 94 were polymorphic bands. Polymorphic information content (PIC) was in the range of 0.332 (initiator 7) to 0.049 (initiator 16). The amount of gene diversity was between 0.126 and 0.977 changes. Cluster analysis of genotypes was done using Jaccard similarity coefficient and UPGMA method 
 

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.

Page 1 from 1     

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.



© 2024 CC BY-NC 4.0 | Nova Biologica Reperta

Designed & Developed by : Yektaweb