Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests
The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral ch...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/9/2337 |
| _version_ | 1827727585242513408 |
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| author | Yue Yu Haiye Yu Xiaokai Li Lei Zhang Yuanyuan Sui |
| author_facet | Yue Yu Haiye Yu Xiaokai Li Lei Zhang Yuanyuan Sui |
| author_sort | Yue Yu |
| collection | DOAJ |
| description | The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R<sup>2</sup> values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R<sup>2</sup> and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium. |
| first_indexed | 2024-03-10T23:08:29Z |
| format | Article |
| id | doaj.art-61218cc3037444a5bad3490f36613458 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-10T23:08:29Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-61218cc3037444a5bad3490f366134582023-11-19T09:10:51ZengMDPI AGAgronomy2073-43952023-09-01139233710.3390/agronomy13092337Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random ForestsYue Yu0Haiye Yu1Xiaokai Li2Lei Zhang3Yuanyuan Sui4School of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaSchool of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaSchool of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaSchool of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaSchool of Biological and Agricultural Engineering, Jilin University, Changchun 130022, ChinaThe information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R<sup>2</sup> values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R<sup>2</sup> and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.https://www.mdpi.com/2073-4395/13/9/2337random forestsfeature selectiontransformed spectravegetation indexpotassium contentrice |
| spellingShingle | Yue Yu Haiye Yu Xiaokai Li Lei Zhang Yuanyuan Sui Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests Agronomy random forests feature selection transformed spectra vegetation index potassium content rice |
| title | Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests |
| title_full | Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests |
| title_fullStr | Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests |
| title_full_unstemmed | Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests |
| title_short | Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests |
| title_sort | prediction of potassium content in rice leaves based on spectral features and random forests |
| topic | random forests feature selection transformed spectra vegetation index potassium content rice |
| url | https://www.mdpi.com/2073-4395/13/9/2337 |
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