Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy
Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Present...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1334961/full |
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| author | Qingxu Li Qingxu Li Wanhuai Zhou Wanhuai Zhou Xuedong Zhang Hao Li Mingjie Li Houjun Liang |
| author_facet | Qingxu Li Qingxu Li Wanhuai Zhou Wanhuai Zhou Xuedong Zhang Hao Li Mingjie Li Houjun Liang |
| author_sort | Qingxu Li |
| collection | DOAJ |
| description | Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities. |
| first_indexed | 2024-03-08T11:43:50Z |
| format | Article |
| id | doaj.art-cadd41a2be574b93aa7d977708571801 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-08T11:43:50Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-cadd41a2be574b93aa7d9777085718012024-01-25T04:21:55ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-01-011510.3389/fpls.2024.13349611334961Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopyQingxu Li0Qingxu Li1Wanhuai Zhou2Wanhuai Zhou3Xuedong Zhang4Hao Li5Mingjie Li6Houjun Liang7College of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaInstitute of Cotton Engineering, Anhui University of Finance & Economics, Bengbu, ChinaCollege of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaInstitute of Cotton Engineering, Anhui University of Finance & Economics, Bengbu, ChinaCollege of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaCollege of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaCollege of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaCollege of Computer Science, Anhui University of Finance & Economics, Bengbu, ChinaWidespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.https://www.frontiersin.org/articles/10.3389/fpls.2024.1334961/fullseed cottonimpuritycotton-netnear-infrared spectroscopyrapid detection |
| spellingShingle | Qingxu Li Qingxu Li Wanhuai Zhou Wanhuai Zhou Xuedong Zhang Hao Li Mingjie Li Houjun Liang Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy Frontiers in Plant Science seed cotton impurity cotton-net near-infrared spectroscopy rapid detection |
| title | Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy |
| title_full | Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy |
| title_fullStr | Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy |
| title_full_unstemmed | Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy |
| title_short | Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy |
| title_sort | cotton net efficient and accurate rapid detection of impurity content in machine picked seed cotton using near infrared spectroscopy |
| topic | seed cotton impurity cotton-net near-infrared spectroscopy rapid detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1334961/full |
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