Determination of olive cultivars by deep learning and ISSR markers

Sesli, Meltem; Yeğenoğlu, E. Dilşat; Altıntas, V.

doi:10.22438/jeb/41/2(si)/jeb-22

Cited by 7 publications

(5 citation statements)

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“…4. As reported previously, ISSR primers are attached in repeat nucleotide regions in the genome, therefore; they comprise 2-5 nucleotides with a haphazard attaching sequence of 1-3 nucleotides (Lin et al, 2012;Sesli et al, 2020). In the current study, 90% of the eight primers were constructed as dinucleotides (Table 1).…”

Section: O N L I N E C O P ÿmentioning

confidence: 53%

“…In the current study, 90% of the eight primers were constructed as dinucleotides (Table 1). Previous studies have documented that repeat dinucleotide ISSR markers are more effective in identifying molecular diversity between the individuals of same species than other nucleotide repeats (Lin et al, 2012;Gaber et al, 2020;Sesli et al, 2020). The number of amplified fragments scored for each ISSR primer was recorded (Table 1).…”

Section: O N L I N E C O P ÿmentioning

confidence: 99%

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The efficacy of COI barcoding and ISSR markers in molecular identification of diverse bird Sternula saundersi populations along the Red Sea coast, Kingdom of Saudi Arabia

Almalki¹,

Ismail²,

Gaber³

2021

JEB

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Aim: To demonstrate the first DNA barcode and molecular diversity of bird Sternula saundersi (Saunders’s tern) populations collected from the Red Sea coast at the Kingdom of Saudi Arabia. Methodology: The mitochondrial cytochrome c oxidase subunit I (COI) gene was isolated and sequenced from 26 different individuals of S. saundersi, whose blood samples were collected from two different areas at the Red Sea coast of the Kingdom of Saudi Arabia. Also, the genetic diversity of 26 individuals were investigated using highly polymorphic nuclear markers, i.e., inter simple sequence repeat [ISSR] technique. Results: The COI gene sequences of all 26 individuals were documented and submitted to the GenBank database. Five unique single nucleotide polymorphisms were found in all individuals of S. saundersi that were not observed in other species of the genus Sternula. Consequently, these five nucleotides can be used as a distinctive DNA fingerprint of S. saundersi. Additionally, it was possible to identify molecular diversity within different populations from two different areas using eight different ISSR markers. Interpretation: The present data demonstrate the power of COI marker gene in identifying S. saundersi, i.e., it can be utilized to discriminate between different species of the genus Sternula. In addition, highly polymorphic nuclear markers (ISSR) were demonstrated to have significant power to accurately distinguish between individuals collected from different widely spaced areas.

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Section: O N L I N E C O P ÿmentioning

confidence: 53%

Section: O N L I N E C O P ÿmentioning

confidence: 99%

The efficacy of COI barcoding and ISSR markers in molecular identification of diverse bird Sternula saundersi populations along the Red Sea coast, Kingdom of Saudi Arabia

Almalki¹,

Ismail²,

Gaber³

2021

JEB

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“…Productivity of a molecular marker technique depends on the amount of polymorphism it can detect among the genotypes under investigation. Inter-simple sequence repeats (ISSRs) have been widely employed to reveal levels and patterns of genetic variation in plants (Khalik et al, 2014;Sesli et al, 2020) to determine evolutionary relationships and to understand levels of genetic variation among wild and cultivated populations ( Wu et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Analysis of genetic diversity among different Rose (Rosa) genotypes using morphological and molecular markers (ISSR)

Raina¹,

Grewal²

2021

Int. J. Curr. Res. Biosci. Plant Biol.

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Rose is a woody perennial of the genus Rosa within the family Rosaceae. In this study, phylogenetic relationship among seven different genotypes of Rose (Rosa) was determined using ISSR primers and morphological parameters. DNA was isolated using CTAB method with 0.5% of CTAB concentration. Out of 50 ISSR primers, 16 primers gave reproducible banding pattern with all the seven genotypes of Rose while others failed to produce polymorphism. A total of 108 bands were obtained with an average polymorphism of 54.69% and an average of 9.06 bands per primer. The average similarity was found to be 0.69 among all the seven genotypes using Jaccard’s similarity coefficient. On basis of polymorphism cluster analysis divided them into two major groups separating one genotype (light pink) from rest of the six genotypes and principle coordinate analysis confirmed the results. A parallel correlation was found between the morphological parameter (flower diameter) and the results of ISSR analysis as both of them divided the seven genotypes into two main groups.

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“…This type of technique achieves high rates of effectiveness, however, as this is not the focus of the article, a second research was carried out in which the term "learning" was added to the terms previously used. This second iteration resulted in an article that fits the theme, in the article [36], the authors present a method of identifying olive tree cultivars using deep learning algorithms and ISSR markers. They use a dataset with 800 training and 200 test images to train a convolutional neural network.…”

Section: Related Workmentioning

confidence: 98%

Machine Learning to Identify Olive-Tree Cultivars

Mendes

Lima

Costa

et al. 2022

Communications in Computer and Information Science

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The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Trás-os-Montes region in Portugal are identified (Cobrançosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

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Determination of olive cultivars by deep learning and ISSR markers

Cited by 7 publications

References 0 publications

The efficacy of COI barcoding and ISSR markers in molecular identification of diverse bird Sternula saundersi populations along the Red Sea coast, Kingdom of Saudi Arabia

The efficacy of COI barcoding and ISSR markers in molecular identification of diverse bird Sternula saundersi populations along the Red Sea coast, Kingdom of Saudi Arabia

Analysis of genetic diversity among different Rose (Rosa) genotypes using morphological and molecular markers (ISSR)

Machine Learning to Identify Olive-Tree Cultivars

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