بررسی تنوع صفات برگ و شناسایی رقم انگور از طریق شکل برگ با استفاده از پردازش تصویر

Introduction

Grapevine is a plant belonging to the genus Vitis and belonging to the Vitaceae family, as one of the most common garden products in the world and in Iran. The high distribution of grapes in the world (Southern Europe, Asia Minor, East Asia and North and Central America) may be due to its high level of biodiversity. Grapes are one of the most important fruit products in Iran, which are very important for fresh consumption and raisins. With the production of about two million tons of grapes, Iran ranks ninth in the world among the countries that produce this product. In this way, identifying the most and most stable phenotypic traits in grapes is one of the main goals in evaluating the biodiversity of grapes. The unique aspect of grape leaves in terms of morphological diversity is having significant diversity compared to other products. Recently, a high heritability test for grape leaves has been carried out on a genetic basis based on leaf morphological characteristics. Identifying the most and most stable phenotypic traits in grapes is one of the main goals in evaluating the biodiversity of grapes, and it is necessary to conduct such studies with accurate biometric software. The purpose of this study is to investigate the diversity of leaf traits and to identify grape varieties through leaf shape using image processing in a computer environment.

Materials and Methods

In this part of the research, 3 mature and healthy leaves from the middle parts of the one-year branches were taken non-randomly and purposefully from 24 grape varieties that were identified from different orchards in Maragheh and the suburbs. The samples were photographed on a white background. Then, it was photographed with a 12 mega pixel camera using shadowless and coaxial light. The samples were photographed by special computer photography software in size 768x1024 with 300 dpi resolution. Finally, using ImageJ software, 14 variables for each photo (leaf area, leaf circumference, average red-green-blue color space (RGB), average intensity of red (R), average intensity of green (G), average intensity of blue (B) ), length, width, color, roundness, left, right, top and bottom) morphometrics were obtained, which were tested and analyzed in the next steps. The data obtained from the present study were analyzed using SPSS var21.0 software based on a completely randomized design. Image J8.0 software was used to analyze the images. In the first step, a simple analysis of variance was performed for the measured traits, and the average data was compared by Duncan's multiple range test. Excel software was used to draw graphs.



Results and Discussion

The results of the variance analysis of agricultural traits data showed that the difference between genotypes was significant among all traits, which indicates a high genetic diversity among the investigated genotypes. According to the simple correlation between the studied traits, the highest correlation between x7 and x8 traits was obtained with a value of 0.994 and the lowest correlation value between x5 and x14 traits or zero value was obtained. In the present study, the results of grouping by cluster analysis were consistent with the results of analysis into principal components. The results of this research showed that x9 variable (Hue) has a different discriminating power in terms of the separation of the studied germplasm, and in other words, it can create a distinct grouping.



Conclusions

In the results of this research, a large variety of changes among the genotypes or cultivars investigated in terms of the measured traits was observed, which indicates the high genetic potential among the cultivars. According to the variance analysis table of grape leaf traits, all traits were significant at the statistical probability level of 1%. The results of analysis into components showed that the first four components have characteristic root values above one and in total justify 95% of the changes in leaf morphometric data. Also, according to the grouping of studied genotypes, they have different leaf morphometric characteristics and therefore they are placed in different groups. For example, G7 is different from other genotypes and is placed in a separate group The grouping of the morphometric variables measured in the leaves of the studied grape genotypes showed that the variable x9 (Hue) has different discriminating power in terms of the separation of the studied germplasm, and in other words, it can create a distinct grouping.

Keywords: Grape genetic diversity, leaf recognition, RGB color spectrum, hue color index