Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864824000671 |
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| author | Yeqi Fei Zhenye Li Tingting Zhu Zengtao Chen Chao Ni |
| author_facet | Yeqi Fei Zhenye Li Tingting Zhu Zengtao Chen Chao Ni |
| author_sort | Yeqi Fei |
| collection | DOAJ |
| description | The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing. |
| format | Article |
| id | doaj-art-e0a9cab798cb49a6a2368694c8113dd1 |
| institution | OA Journals |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-e0a9cab798cb49a6a2368694c8113dd12025-08-20T01:49:01ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111230831610.1016/j.dcan.2024.05.008Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural networkYeqi Fei0Zhenye Li1Tingting Zhu2Zengtao Chen3Chao Ni4College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; School of Intelligent Manufacturing, Nanjing University of Science and Technology ZiJin College, Nanjing 210046, China; Department of Mechanical Engineering, University of Alberta, Edmonton AB T6G 1H9, CanadaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; Department of Mechanical Engineering, University of Alberta, Edmonton AB T6G 1H9, Canada; Corresponding authors at: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; Corresponding authors at: College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles, and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles. By fusing band combination optimization with deep learning, this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line. By applying hyperspectral imaging and a one-dimensional deep learning algorithm, we detect and classify impurities in seed cotton after harvest. The main categories detected include pure cotton, conveyor belt, film covering seed cotton, and film adhered to the conveyor belt. The proposed method achieves an impurity detection rate of 99.698%. To further ensure the feasibility and practical application potential of this strategy, we compare our results against existing mainstream methods. In addition, the model shows excellent recognition performance on pseudo-color images of real samples. With a processing time of 11.764 μs per pixel from experimental data, it shows a much improved speed requirement while maintaining the accuracy of real production lines. This strategy provides an accurate and efficient method for removing impurities during cotton processing.http://www.sciencedirect.com/science/article/pii/S2352864824000671Seed cottonFilm impurityHyperspectral imagingBand optimizationClassification |
| spellingShingle | Yeqi Fei Zhenye Li Tingting Zhu Zengtao Chen Chao Ni Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network Digital Communications and Networks Seed cotton Film impurity Hyperspectral imaging Band optimization Classification |
| title | Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network |
| title_full | Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network |
| title_fullStr | Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network |
| title_full_unstemmed | Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network |
| title_short | Nondestructive detection and classification of impurities-containing seed cotton based on hyperspectral imaging and one-dimensional convolutional neural network |
| title_sort | nondestructive detection and classification of impurities containing seed cotton based on hyperspectral imaging and one dimensional convolutional neural network |
| topic | Seed cotton Film impurity Hyperspectral imaging Band optimization Classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352864824000671 |
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