A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm
文献类型：期刊论文
| 作者 | Zhao, Guangxia1,2; Xu, Zhuopin2; Tang, Liwen2,3; Li, Xiaohong1,2; Zhang, Pengfei2; Wang, Qi2
|
| 刊名 | MICROCHEMICAL JOURNAL
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 205 |
| 关键词 | Sorghum variety classification Hyperspectral imaging Convolutional neural network Successive projections algorithm |
| ISSN号 | 0026-265X |
| DOI | 10.1016/j.microc.2024.111095 |
| 通讯作者 | Wang, Qi(wangqi@ipp.ac.cn) |
| 英文摘要 | Accurate classification of sorghum varieties is crucial to the production and processing of liquor with sorghum as raw materials. Hyperspectral imaging (HSI) has the potential to achieve this goal quickly and nondestructively. This study proposes a novel algorithm, an improved Principal Component Analysis combined with SpectrumImage-Convolutional Neural Network (PCA-SICNN), which can combine spectral features and image features of HSI data, to enhance the accuracy of variety identification of sorghum seeds. To verify the effect of this algorithm, hyperspectral imaging data (939-1700 nm) of 13,200 sorghum seeds from 6 varieties were collected. The principal component analysis (PCA) was employed to select 20-dimension images from the origin hyperspectral imaging data. Spectrum-Image-Convolutional Neural Network (SICNN) extracts the spectral and image features of sorghum in the network and then fuses the features. By fully learning the HSI data features of sorghum, it achieves the classification of sorghum varieties. The results demonstrate that PCA-SICNN achieves an accuracy on the training set and on the test set reaches 98.67 % and 98.64 %, respectively. Compared with other control methods, the prediction accuracy of the PCA-SICNN model increased by at least 1.10 %. These results suggest the potential for the method to be widely applied in the production and processing of sorghum. |
| 资助项目 | National Natural Science Foundation of China[32070399] ; Major Special Project of Anhui Province[2021d06050003] ; Anhui Science and Technology Major Project[202103a06020014] |
| WOS研究方向 | Chemistry |
| 语种 | 英语 |
| WOS记录号 | WOS:001273529100001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Major Special Project of Anhui Province ; Anhui Science and Technology Major Project |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/137277]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wang, Qi |
| 作者单位 | 1.Univ Sci & Technol China, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Guangxia,Xu, Zhuopin,Tang, Liwen,et al. A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm[J]. MICROCHEMICAL JOURNAL,2024,205. |
| APA | Zhao, Guangxia,Xu, Zhuopin,Tang, Liwen,Li, Xiaohong,Zhang, Pengfei,&Wang, Qi.(2024).A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm.MICROCHEMICAL JOURNAL,205. |
| MLA | Zhao, Guangxia,et al."A rapid classification method for sorghum seed varieties based on HSI and PCA-SICNN algorithm".MICROCHEMICAL JOURNAL 205(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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