Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes
Year: 2024
Type: article
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.
Source: Scientific Reports
Institutions: Chaudhary Charan Singh Haryana Agricultural University, Sri Karan Narendra Agriculture University, Jobner, Indian Institute of Wheat and Barley Research, Govind Ballabh Pant University of Agriculture and Technology, King Khalid University, Princess Nourah bint Abdulrahman University, University of Ha'il, Kyonggi University, Al-Ayen Iraqi University, Parul University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,590
OpenAlex ID: W4402980729