Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology
This paper offers a prospective solution to the poor quality and less prominent features of the original terahertz spectral images of unsound wheat grains caused due to the imaging system and background noise. In this paper, a CBDNet-V terahertz spectral image enhancement model is proposed. Compared...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/5/1093 |
| _version_ | 1827670974899683328 |
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| author | Yuying Jiang Fei Wang Hongyi Ge Guangming Li Xinyu Chen Li Li Ming Lv Yuan Zhang |
| author_facet | Yuying Jiang Fei Wang Hongyi Ge Guangming Li Xinyu Chen Li Li Ming Lv Yuan Zhang |
| author_sort | Yuying Jiang |
| collection | DOAJ |
| description | This paper offers a prospective solution to the poor quality and less prominent features of the original terahertz spectral images of unsound wheat grains caused due to the imaging system and background noise. In this paper, a CBDNet-V terahertz spectral image enhancement model is proposed. Compared with the traditional algorithms, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the obtained enhanced images using the proposed model show performance improvement. As validated by the ResNet-50 classification network, the proposed model processes images with an accuracy of 94.8%, and the recognition accuracy is improved by 3.7% and 1.9%, respectively, compared to the images with only denoising and feature extraction. The experimental results indicate that the deep learning-based terahertz spectral image technology for unsound wheat kernels has good prospects in the identification of unsound wheat kernels. |
| first_indexed | 2024-03-10T03:30:39Z |
| format | Article |
| id | doaj.art-beb47f6bb0b94575b1fd8c4ffb810020 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T03:30:39Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-beb47f6bb0b94575b1fd8c4ffb8100202023-11-23T09:42:41ZengMDPI AGAgronomy2073-43952022-04-01125109310.3390/agronomy12051093Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging TechnologyYuying Jiang0Fei Wang1Hongyi Ge2Guangming Li3Xinyu Chen4Li Li5Ming Lv6Yuan Zhang7Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaKey Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, ChinaThis paper offers a prospective solution to the poor quality and less prominent features of the original terahertz spectral images of unsound wheat grains caused due to the imaging system and background noise. In this paper, a CBDNet-V terahertz spectral image enhancement model is proposed. Compared with the traditional algorithms, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the obtained enhanced images using the proposed model show performance improvement. As validated by the ResNet-50 classification network, the proposed model processes images with an accuracy of 94.8%, and the recognition accuracy is improved by 3.7% and 1.9%, respectively, compared to the images with only denoising and feature extraction. The experimental results indicate that the deep learning-based terahertz spectral image technology for unsound wheat kernels has good prospects in the identification of unsound wheat kernels.https://www.mdpi.com/2073-4395/12/5/1093unsound wheat kernelsterahertz imagesimage processingdeep learning |
| spellingShingle | Yuying Jiang Fei Wang Hongyi Ge Guangming Li Xinyu Chen Li Li Ming Lv Yuan Zhang Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology Agronomy unsound wheat kernels terahertz images image processing deep learning |
| title | Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology |
| title_full | Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology |
| title_fullStr | Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology |
| title_full_unstemmed | Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology |
| title_short | Identification of Unsound Grains in Wheat Using Deep Learning and Terahertz Spectral Imaging Technology |
| title_sort | identification of unsound grains in wheat using deep learning and terahertz spectral imaging technology |
| topic | unsound wheat kernels terahertz images image processing deep learning |
| url | https://www.mdpi.com/2073-4395/12/5/1093 |
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