The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM
Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex oper...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/13/4091 |
| _version_ | 1797408576802652160 |
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| author | Yan He Wei Zhang Yongcai Ma Jinyang Li Bo Ma |
| author_facet | Yan He Wei Zhang Yongcai Ma Jinyang Li Bo Ma |
| author_sort | Yan He |
| collection | DOAJ |
| description | Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm<sup>−1</sup>) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds. |
| first_indexed | 2024-03-09T04:01:31Z |
| format | Article |
| id | doaj.art-72af89325047472cbe8b1fa017e6c45d |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T04:01:31Z |
| publishDate | 2022-06-01 |
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| series | Molecules |
| spelling | doaj.art-72af89325047472cbe8b1fa017e6c45d2023-12-03T14:13:19ZengMDPI AGMolecules1420-30492022-06-012713409110.3390/molecules27134091The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVMYan He0Wei Zhang1Yongcai Ma2Jinyang Li3Bo Ma4Engineering College, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaEngineering College, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaEngineering College, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaEngineering College, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaQiqihar Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, ChinaRice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm<sup>−1</sup>) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.https://www.mdpi.com/1420-3049/27/13/4091ranman spectroscopyrice blastresistant varietiesoptimize support vector machine algorithmartificial bee colony algorithm |
| spellingShingle | Yan He Wei Zhang Yongcai Ma Jinyang Li Bo Ma The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM Molecules ranman spectroscopy rice blast resistant varieties optimize support vector machine algorithm artificial bee colony algorithm |
| title | The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM |
| title_full | The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM |
| title_fullStr | The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM |
| title_full_unstemmed | The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM |
| title_short | The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM |
| title_sort | classification of rice blast resistant seed based on ranman spectroscopy and svm |
| topic | ranman spectroscopy rice blast resistant varieties optimize support vector machine algorithm artificial bee colony algorithm |
| url | https://www.mdpi.com/1420-3049/27/13/4091 |
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