ArticleScientific reports2024
Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.BMC plant biology · 2026Article
- A Review of Machine Learning and AI Applications in Enhancing HACCP Systems for Ice Cream Manufacturing.Foods (Basel, Switzerland) · 2026Review
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Intelligent Decision Support for Transcatheter Aortic Valve Replacement: Machine Learning Spans From Anatomical Assessment to Dynamic Risk Modeling.Reviews in cardiovascular medicine · 2026Review
- Optimized wheat seed classification using YOLO with morphological image feature enhancement.Scientific reports · 2026Article
- Optimized deep learning framework for pomegranate disease detection using nature-inspired algorithms.Plant methods · 2025Article
- Article
- Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.Scientific reports · 2025Article
- Support Vector Machines in Polymer Science: A Review.Polymers · 2025Review
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
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Authors and funding
14 authors.
Funding
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.
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Registered trials
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