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

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

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Publication Details

Journal
Scientific Reports
Published
2024-09-30
DOI
https://doi.org/10.1038/s41598-024-72056-0
Citations
18
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
4.24

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article

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

Jeong Ryeol Choi, Mujahid Khan, Arpit Gaur, Krishna Kumar Yadav et al.
18 citations
Scientific Reports
Spectroscopy and Chemometric Analyses
4.24
article

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

Jeong Ryeol Choi, Mujahid Khan, Arpit Gaur, Krishna Kumar Yadav, Sonia Sheoran, Mohammad Khalid, B. K. Hooda, Dinesh Kumar Vishwakarma, Hemender Tanwar, Yogesh Jindal, Maha Awjan Alreshidi, Ghadah Shukri Albakri, Vikram Singh, Sushma Sharma
article en
18 citations

Abstract

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

Scientific ReportsVol. 14(1)
Princess Nourah bint Abdulrahman University (SA), Thi Qar University (IQ), Chaudhary Charan Singh Haryana Agricultural University (IN), Govind Ballabh Pant University of Agriculture and Technology (IN), Swami Keshwanand Rajasthan Agricultural University (IN), Kyonggi University (KR), University of Ha'il (SA), Indian Institute of Wheat and Barley Research (IN), King Khalid University (SA), Parul University (IN)
National Research Foundation, National Research Foundation of Korea, Princess Nourah Bint Abdulrahman University, King Khalid University, Ministry of Science and ICT, South Korea
Zero hunger
Openalex Percentile: Top 5%
Spectroscopy and Chemometric Analyses
4.24
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