| Autor: |
Mujahid Khan, B. K. Hooda, Arpit Gaur, Vikram Singh, Yogesh Jindal, Hemender Tanwar, Sushma Sharma, Sonia Sheoran, Dinesh Kumar Vishwakarma, Mohammad Khalid, Ghadah Shukri Albakri, Maha Awjan Alreshidi, Jeong Ryeol Choi, Krishna Kumar Yadav |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-72056-0 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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