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Updated: Jun 11, 2025

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Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.

Mujahid Khan1,2, B K Hooda2, Arpit Gaur3,4

  • 1Agricultural Research Station (SKNAU, Jobner), Fatehpur-Shekhawati, Sikar, 332301, India.

Scientific Reports
|September 30, 2024
PubMed
Summary

This study enhanced wheat genotype classification using Support Vector Machines (SVMs) with optimization techniques. Particle Swarm Optimization and Radial Basis Function kernels achieved 94.9% accuracy, aiding crop improvement.

Keywords:
Ensemble algorithmEnsemble weighted average (EWA)Radial basis functionSupport vector machineWheat genotypes classification

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Area of Science:

  • Agricultural Science
  • Computational Biology
  • Machine Learning

Background:

  • Accurate classification of wheat genotypes is crucial for crop improvement and breeding programs.
  • Traditional methods may not fully leverage complex genotypic and phenotypic data.
  • Machine learning offers advanced tools for analyzing large agricultural datasets.

Purpose of the Study:

  • To classify 302 wheat genotypes using Support Vector Machines (SVMs).
  • To enhance SVM classification accuracy through ensemble algorithms and optimization techniques.
  • To evaluate the effectiveness of different SVM kernels and optimization methods for wheat genotype identification.

Main Methods:

  • Utilized a dataset of 302 wheat genotypes and 14 morphological attributes.
  • Evaluated six Support Vector Machine (SVM) kernels: linear, radial basis function (RBF), sigmoid, and polynomial (degrees 1-3).
  • Applied optimization techniques including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization (PSO).
  • Employed weighted accuracy ensemble methods to further improve classification performance.

Main Results:

  • The Radial Basis Function (RBF) kernel achieved the highest initial accuracy of 93.2%.
  • Ensemble methods, specifically weighted accuracy ensemble, improved performance to 94.9%.
  • Optimization-based SVM classification, particularly with Particle Swarm Optimization (PSO), yielded a significant 1.7% accuracy gain on the test set, reaching 94.9%.

Conclusions:

  • Support Vector Machines (SVMs), especially with RBF kernels and optimization techniques like PSO, are highly effective for wheat genotype classification.
  • These computational approaches significantly enhance accuracy in agricultural research.
  • The findings demonstrate the potential of advanced machine learning for accelerating crop improvement and breeding efforts.