Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging
[Display omitted] •Hyperspectral image applied for non-destructive detection of Zn content in plants.•An MSSAE was proposed for extracting deep features from hyperspectral data.•The model based on the deep features extracted by MSSAE had the best prediction. Zinc (Zn) content plays a decisive role i...
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| Vydané v: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Ročník 281; s. 121641 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
15.11.2022
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| ISSN: | 1386-1425, 1873-3557, 1873-3557 |
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| Abstract | [Display omitted]
•Hyperspectral image applied for non-destructive detection of Zn content in plants.•An MSSAE was proposed for extracting deep features from hyperspectral data.•The model based on the deep features extracted by MSSAE had the best prediction.
Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431–962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves. |
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| AbstractList | [Display omitted]
•Hyperspectral image applied for non-destructive detection of Zn content in plants.•An MSSAE was proposed for extracting deep features from hyperspectral data.•The model based on the deep features extracted by MSSAE had the best prediction.
Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431–962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves. Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves.Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed rape industry. This study adopted a deep learning (DL) method to predict the Zn content of oilseed rape leaves using hyperspectral imaging (HSI). The dropout mechanism was introduced to improve the stacked sparse autoencoder (SSAE) and named modified SSAE (MSSAE). MSSAE extracted deep spectral features of samples based on pixel-level spectral information (the wavelength range of the spectrum is 431-962 nm). Subsequently, the deep spectral features were applied as the inputs for support vector regression (SVR) and least squares support vector regression (LSSVR) to predict the Zn content in oilseed rape leaves. In addition, the successive projections algorithm (SPA) and the variable iterative space shrinkage approach (VISSA) were investigated as wavelength selection algorithms for comparison. The results showed that the MSSAE-LSSVR model had the best prediction performance (the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9566 and 1.0240 mg/kg, respectively). The overall results showed that the MSSAE was able to extract the deep features of HSI data and validated the possibility of HSI combined with a DL method for nondestructive testing of Zn content in oilseed rape leaves. |
| ArticleNumber | 121641 |
| Author | Sun, Jun Wang, Simin Yao, Kunshan Zhou, Xin Fu, Lvhui Xu, Min |
| Author_xml | – sequence: 1 givenname: Lvhui surname: Fu fullname: Fu, Lvhui – sequence: 2 givenname: Jun surname: Sun fullname: Sun, Jun email: sun2000jun@sina.com – sequence: 3 givenname: Simin surname: Wang fullname: Wang, Simin – sequence: 4 givenname: Min surname: Xu fullname: Xu, Min – sequence: 5 givenname: Kunshan surname: Yao fullname: Yao, Kunshan – sequence: 6 givenname: Xin surname: Zhou fullname: Zhou, Xin |
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| CitedBy_id | crossref_primary_10_1016_j_foodcont_2024_110763 crossref_primary_10_1016_j_saa_2024_125212 crossref_primary_10_1109_ACCESS_2025_3587226 crossref_primary_10_1016_j_heliyon_2024_e25844 crossref_primary_10_1016_j_saa_2025_126652 crossref_primary_10_1016_j_scitotenv_2024_175076 crossref_primary_10_1002_saj2_70095 crossref_primary_10_1016_j_saa_2025_126387 crossref_primary_10_1016_j_jfca_2025_107835 crossref_primary_10_3390_horticulturae11070840 crossref_primary_10_1016_j_scitotenv_2022_160652 crossref_primary_10_1016_j_saa_2022_121940 crossref_primary_10_1016_j_biosystemseng_2024_08_008 crossref_primary_10_1016_j_microc_2023_109306 crossref_primary_10_1016_j_saa_2023_122337 crossref_primary_10_1007_s11694_023_02044_x crossref_primary_10_1016_j_compag_2023_108577 crossref_primary_10_1016_j_foodchem_2025_144055 crossref_primary_10_1080_01431161_2023_2295832 crossref_primary_10_3390_agronomy15092229 crossref_primary_10_1016_j_compag_2023_107920 crossref_primary_10_3390_bioengineering12090966 crossref_primary_10_1007_s10921_024_01049_w |
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| Keywords | Oilseed rape Modified stacked sparse autoencoder Zinc content Dropout Hyperspectral imaging |
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•Hyperspectral image applied for non-destructive detection of Zn content in plants.•An MSSAE was proposed for extracting deep features from... Zinc (Zn) content plays a decisive role in plant growth. Accurate management of Zn fertilizer application can promote high-quality development of the oilseed... |
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| SubjectTerms | Dropout Hyperspectral imaging Modified stacked sparse autoencoder Oilseed rape Zinc content |
| Title | Nondestructive evaluation of Zn content in rape leaves using MSSAE and hyperspectral imaging |
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