Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning
Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of ap...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1492059/full |
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| author | Chenbo Yang Chenbo Yang Meichen Feng Juan Bai Hui Sun Rutian Bi Lifang Song Chao Wang Yu Zhao Wude Yang Lujie Xiao Meijun Zhang Xiaoyan Song |
| author_facet | Chenbo Yang Chenbo Yang Meichen Feng Juan Bai Hui Sun Rutian Bi Lifang Song Chao Wang Yu Zhao Wude Yang Lujie Xiao Meijun Zhang Xiaoyan Song |
| author_sort | Chenbo Yang |
| collection | DOAJ |
| description | Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance. The original spectral reflectance curves were preprocessed using 3 FOD methods: Grünwald-Letnikov (GL), Riemann-Liouville (RL), and Caputo. Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). The main results were as follows: For the 3 types of FOD, GL-FOD was suitable for analyzing the change process of the original spectral curve towards the integer-order derivative spectral curve. RL-FOD was suitable for constructing the hyperspectral monitoring model of winter wheat ChD. Caputo-FOD was not suitable for hyperspectral research due to its insensitivity to changes in order. The 3 FOD calculation methods could all improve the correlation between the original spectral curve and Log(ChD) to varying degrees, but only the GL method and RL method could observe the change process of correlation with order changes, and the shorter the wavelength, the smaller the order, and the higher the correlation. The bands screened by CARS were distributed throughout the entire spectral range, but there was a relatively concentrated distribution in the visible light region. Among all models, CARS was used to screen bands based on the 0.3-order RL-FOD spectrum, and the model constructed using ETsR reached the best accuracy and stability. Its R2c, RMSEc, R2v, RMSEv, and RPD were 1.0000, 0.0000, 0.8667, 0.1732, and 2.6660, respectively. In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. The results showed that based on the 0.3-order RL-FOD, combined with the CARS screening band, ETsR modeling has the highest accuracy, and hyperspectral estimation of winter wheat ChD can be realized. The results of this study can provide some reference for the rapid and nondestructive estimation of ChD in winter wheat. |
| format | Article |
| id | doaj-art-8212dc53a5c5442699d81d9182f37096 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Plant Science |
| spelling | doaj-art-8212dc53a5c5442699d81d9182f370962025-01-14T06:10:35ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.14920591492059Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learningChenbo Yang0Chenbo Yang1Meichen Feng2Juan Bai3Hui Sun4Rutian Bi5Lifang Song6Chao Wang7Yu Zhao8Wude Yang9Lujie Xiao10Meijun Zhang11Xiaoyan Song12College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Resources and Environment, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaLife Sciences Department, Yuncheng University, Yuncheng, Shanxi, ChinaCollege of Resources and Environment, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaCollege of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, ChinaChlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance. The original spectral reflectance curves were preprocessed using 3 FOD methods: Grünwald-Letnikov (GL), Riemann-Liouville (RL), and Caputo. Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). The main results were as follows: For the 3 types of FOD, GL-FOD was suitable for analyzing the change process of the original spectral curve towards the integer-order derivative spectral curve. RL-FOD was suitable for constructing the hyperspectral monitoring model of winter wheat ChD. Caputo-FOD was not suitable for hyperspectral research due to its insensitivity to changes in order. The 3 FOD calculation methods could all improve the correlation between the original spectral curve and Log(ChD) to varying degrees, but only the GL method and RL method could observe the change process of correlation with order changes, and the shorter the wavelength, the smaller the order, and the higher the correlation. The bands screened by CARS were distributed throughout the entire spectral range, but there was a relatively concentrated distribution in the visible light region. Among all models, CARS was used to screen bands based on the 0.3-order RL-FOD spectrum, and the model constructed using ETsR reached the best accuracy and stability. Its R2c, RMSEc, R2v, RMSEv, and RPD were 1.0000, 0.0000, 0.8667, 0.1732, and 2.6660, respectively. In conclusion, based on the winter wheat ChD data set and the corresponding canopy hyperspectral data set, combined with 3 FOD calculation methods, 1 band screening method, and 8 modeling algorithms, this study constructed hyperspectral monitoring models for winter wheat ChD. The results showed that based on the 0.3-order RL-FOD, combined with the CARS screening band, ETsR modeling has the highest accuracy, and hyperspectral estimation of winter wheat ChD can be realized. The results of this study can provide some reference for the rapid and nondestructive estimation of ChD in winter wheat.https://www.frontiersin.org/articles/10.3389/fpls.2024.1492059/fullhyperspectralchlorophyll densityfractional-order derivativecompetitive adaptive reweighted samplingmachine learning |
| spellingShingle | Chenbo Yang Chenbo Yang Meichen Feng Juan Bai Hui Sun Rutian Bi Lifang Song Chao Wang Yu Zhao Wude Yang Lujie Xiao Meijun Zhang Xiaoyan Song Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning Frontiers in Plant Science hyperspectral chlorophyll density fractional-order derivative competitive adaptive reweighted sampling machine learning |
| title | Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning |
| title_full | Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning |
| title_fullStr | Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning |
| title_full_unstemmed | Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning |
| title_short | Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning |
| title_sort | hyperspectral estimation of chlorophyll density in winter wheat using fractional order derivative combined with machine learning |
| topic | hyperspectral chlorophyll density fractional-order derivative competitive adaptive reweighted sampling machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1492059/full |
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