Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion
The nitrogen content is an important indicator affecting corn plants’ growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1910 |
| _version_ | 1797625105957781504 |
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| author | Ling Wu Yuanjuan Gong Xiaoping Bai Wei Wang Zhuo Wang |
| author_facet | Ling Wu Yuanjuan Gong Xiaoping Bai Wei Wang Zhuo Wang |
| author_sort | Ling Wu |
| collection | DOAJ |
| description | The nitrogen content is an important indicator affecting corn plants’ growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400~1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves’ nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the R<sub>P</sub><sup>2</sup> and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves. |
| first_indexed | 2024-03-11T09:52:08Z |
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| id | doaj.art-7aba96d3caf04721aebbac9f4d965f07 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:52:08Z |
| publishDate | 2023-02-01 |
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| series | Applied Sciences |
| spelling | doaj.art-7aba96d3caf04721aebbac9f4d965f072023-11-16T16:12:06ZengMDPI AGApplied Sciences2076-34172023-02-01133191010.3390/app13031910Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture FusionLing Wu0Yuanjuan Gong1Xiaoping Bai2Wei Wang3Zhuo Wang4College of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaShenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaThe nitrogen content is an important indicator affecting corn plants’ growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400~1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves’ nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the R<sub>P</sub><sup>2</sup> and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves.https://www.mdpi.com/2076-3417/13/3/1910hyperspectralfusion featurescorn nitrogen contentmachine learningnondestructive detection |
| spellingShingle | Ling Wu Yuanjuan Gong Xiaoping Bai Wei Wang Zhuo Wang Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion Applied Sciences hyperspectral fusion features corn nitrogen content machine learning nondestructive detection |
| title | Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion |
| title_full | Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion |
| title_fullStr | Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion |
| title_full_unstemmed | Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion |
| title_short | Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion |
| title_sort | nondestructive determination of leaf nitrogen content in corn by hyperspectral imaging using spectral and texture fusion |
| topic | hyperspectral fusion features corn nitrogen content machine learning nondestructive detection |
| url | https://www.mdpi.com/2076-3417/13/3/1910 |
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