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Peer-reviewedCC BY-NC-ND 4.0BMC Plant Biology

A hybrid feature selection-based machine learning framework for robust prediction of parthenocarpy in Hibiscus sabdariffa L

  • Razieh Sheikhpour1
  • Heidar Meftahizade2
  • Mojtaba Heydari-Majd3
  1. 1Department of Computer Engineering, Faculty of Engineering, Ardakan University, P.O. Box 184, Ardakan, Iran
  2. 2Department of Horticultural Sciences, Faculty of Agriculture and Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran
  3. 3Faculty of Agriculture and Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran
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Abstract

Modeling the phenotypic expression of parthenocarpy in Hibiscus sabdariffa is important for improving fruit set, enhancing yield stability, and supporting breeding research. This study presents a hybrid feature selection and machine learning framework that integrates filter-based and regularization-based techniques to identify biologically informative phenotypic traits associated with parthenocarpy, improve model interpretability, and reduce feature dimensionality. Regression models, including Support Vector Machine with a radial basis function kernel (SVM-RBF), Decision Tree, Random Forest, Ensemble Learning and Linear Regression, were developed and evaluated using the complete set of 24 experimentally measured morphological, fruit-, capsule-, and seed-related traits, as well as the compact feature subsets identified by the proposed hybrid feature selection framework. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and concordance correlation coefficient (CCC). The results show that high phenotype-based modeling performance can be achieved using fewer, biophysically relevant traits; however, non-linear models consistently outperformed linear models across all evaluated models (test R2 values up to 0.983, CCC > 0.99). Among the traits most strongly associated with the final expression of parthenocarpy were seed-related characteristics (i.e., the number of mature and immature seeds, seed weight and seed size), while fruit-related traits (e.g., fruit and capsule volume) provided secondary information. The proposed framework enables dimensionality reduction, improves model interpretability, and provides an interpretable framework for phenotype-based trait prioritization and modeling of complex reproductive traits in Hibiscus sabdariffa. Rather than serving as an early prediction tool prior to seed development, the framework is intended to facilitate phenotype-based analysis and prioritization of traits associated with the observed expression of parthenocarpy.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09613-4.

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Cite

BibTeXRIS
BibTeX
@article{sheikhpour2026hybrid,
  title = {{A hybrid feature selection-based machine learning framework for robust prediction of parthenocarpy in Hibiscus sabdariffa L}},
  author = {Razieh Sheikhpour and Heidar Meftahizade and Mojtaba Heydari-Majd},
  journal = {BMC Plant Biology},
  year = {2026},
  month = jul,
  doi = {10.1186/s12870-026-09613-4},
  url = {https://doi.org/10.1186/s12870-026-09613-4},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - A hybrid feature selection-based machine learning framework for robust prediction of parthenocarpy in Hibiscus sabdariffa L
AU  - Razieh Sheikhpour
AU  - Heidar Meftahizade
AU  - Mojtaba Heydari-Majd
PY  - 2026
DA  - 2026/07/30
JO  - BMC Plant Biology
DO  - 10.1186/s12870-026-09613-4
UR  - https://doi.org/10.1186/s12870-026-09613-4
AB  - Modeling the phenotypic expression of parthenocarpy in Hibiscus sabdariffa is important for improving fruit set, enhancing yield stability, and supporting breeding research. This study presents a hybrid feature selection and machine learning framework that integrates filter-based and regularization-based techniques to identify biologically informative phenotypic traits associated with parthenocarpy, improve model interpretability, and reduce feature dimensionality. Regression models, including Support Vector Machine with a radial basis function kernel (SVM-RBF), Decision Tree, Random Forest, Ensemble Learning and Linear Regression, were developed and evaluated using the complete set of 24 experimentally measured morphological, fruit-, capsule-, and seed-related traits, as well as the compact feature subsets identified by the proposed hybrid feature selection framework. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and concordance correlation coefficient (CCC). The results show that high phenotype-based modeling performance can be achieved using fewer, biophysically relevant traits; however, non-linear models consistently outperformed linear models across all evaluated models (test R2 values up to 0.983, CCC > 0.99). Among the traits most strongly associated with the final expression of parthenocarpy were seed-related characteristics (i.e., the number of mature and immature seeds, seed weight and seed size), while fruit-related traits (e.g., fruit and capsule volume) provided secondary information. The proposed framework enables dimensionality reduction, improves model interpretability, and provides an interpretable framework for phenotype-based trait prioritization and modeling of complex reproductive traits in Hibiscus sabdariffa. Rather than serving as an early prediction tool prior to seed development, the framework is intended to facilitate phenotype-based analysis and prioritization of traits associated with the observed expression of parthenocarpy.  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09613-4.
SN  - 1471-2229
ER  -