Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach

Akife Dalda Şekerci

doi:10.15832/ankutbd.1904275

Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach

Abstract

Hollyhock (Alcea rosea) is an important ornamental and medicinal plant. This study aimed to evaluate the genetic diversity and population structure of 28 endemic genotypes collected from various regions of Türkiye using an integrated approach combining morphological, biochemical, and molecular markers. Significant morphological variation was observed, and image analysis techniques were employed to classify flower and leaf colors. Biochemical assays revealed substantial diversity in bioactive compound levels, with total phenolic content ranging from 0.18 to 4.65 mg GAE/kg. Antioxidant activities also varied: FRAP (5.8 22.7%), CUPRAC (26.06–78.26%), and DPPH (13.6–70%). Molecular diversity was assessed using iPBS retrotransposon-based markers, with similarity indices ranging from 0.35 to 0.74, aligning with morphological and biochemical patterns. Principal Component Analysis (PCA) indicated dispersed distribution among genotypes in both two- and three dimensional plots, consistent with phenotypic variation. STRUCTURE analysis identified four distinct subpopulations based on genetic structure. Furthermore, machine learning (ML) models-Multilayer Perceptron (MLP), Support Vector Machines (SVM), and Random Forest (RF)-were employed to predict key biochemical and morphological traits. Model performance varied, with different R² values obtained for antioxidant related traits. This is the first comprehensive study of Alcea rosea integrating molecular data and ML tools to characterize genetic diversity and trait prediction.

Keywords

Supporting Institution

Erciyes University

Project Number

2023-12462

Thanks

Erciyes University Scientific Research Projects Coordination Office for supporting this study with the project coded 2023-12462.

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Details

Primary Language

English

Subjects

Plant Biotechnology in Agriculture

Journal Section

Research Article

Authors

Akife Dalda Şekerci ^*
0000-0001-8554-6501
Türkiye

Publication Date

October 12, 2026

Submission Date

March 6, 2026

Acceptance Date

June 23, 2026

Published in Issue

Year 2026 Volume: 32 Number: 4

DOI

https://doi.org/10.15832/ankutbd.1904275

IZ

https://izlik.org/JA93LX88BA

Cite

RIS / Bibtex

APA

Dalda Şekerci, A. (2026). Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach. Journal of Agricultural Sciences, 32(4), 1188-1207. https://doi.org/10.15832/ankutbd.1904275

AMA

1.Dalda Şekerci A. Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach. J Agr Sci-Tarim Bili. 2026;32(4):1188-1207. doi:10.15832/ankutbd.1904275

Chicago

Dalda Şekerci, Akife. 2026. “Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea Rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach”. Journal of Agricultural Sciences 32 (4): 1188-1207. https://doi.org/10.15832/ankutbd.1904275.

EndNote

Dalda Şekerci A (October 1, 2026) Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach. Journal of Agricultural Sciences 32 4 1188–1207.

IEEE

[1]A. Dalda Şekerci, “Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach”, J Agr Sci-Tarim Bili, vol. 32, no. 4, pp. 1188–1207, Oct. 2026, doi: 10.15832/ankutbd.1904275.

ISNAD

Dalda Şekerci, Akife. “Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea Rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach”. Journal of Agricultural Sciences 32/4 (October 1, 2026): 1188-1207. https://doi.org/10.15832/ankutbd.1904275.

JAMA

1.Dalda Şekerci A. Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach. J Agr Sci-Tarim Bili. 2026;32:1188–1207.

MLA

Dalda Şekerci, Akife. “Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea Rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach”. Journal of Agricultural Sciences, vol. 32, no. 4, Oct. 2026, pp. 1188-07, doi:10.15832/ankutbd.1904275.

Vancouver

1.Akife Dalda Şekerci. Machine Learning-Based Assessment of Genetic Diversity and Population Structure in Hollyhock (Alcea rosea): A Comprehensive Morphological, Biochemical, and Molecular Approach. J Agr Sci-Tarim Bili. 2026 Oct. 1;32(4):1188-207. doi:10.15832/ankutbd.1904275