Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral rang...
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Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction e ffect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (r(p)), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.
Accurate classification of sorghum varieties is crucial to the production and processing of liquor with sorghum as raw materials. Hyperspectral imaging (HSI) has the potential to achieve this goal quickly and nondestr...
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Accurate classification of sorghum varieties is crucial to the production and processing of liquor with sorghum as raw materials. Hyperspectral imaging (HSI) has the potential to achieve this goal quickly and nondestructively. This study proposes a novel algorithm, an improved Principal Component Analysis combined with SpectrumImage-Convolutional Neural Network (PCA-SICNN), which can combine spectral features and image features of HSI data, to enhance the accuracy of variety identification of sorghum seeds. To verify the effect of this algorithm, hyperspectral imaging data (939-1700 nm) of 13,200 sorghum seeds from 6 varieties were collected. The principal component analysis (PCA) was employed to select 20-dimension images from the origin hyperspectral imaging data. Spectrum-Image-Convolutional Neural Network (SICNN) extracts the spectral and image features of sorghum in the network and then fuses the features. By fully learning the HSI data features of sorghum, it achieves the classification of sorghum varieties. The results demonstrate that PCA-SICNN achieves an accuracy on the training set and on the test set reaches 98.67 % and 98.64 %, respectively. Compared with other control methods, the prediction accuracy of the PCA-SICNN model increased by at least 1.10 %. These results suggest the potential for the method to be widely applied in the production and processing of sorghum.
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella *** component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by t...
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Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella *** component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal ***-square support vector machine (LS-SVM) was used to establish the calibration *** projectionsalgorithm (SPA) was applied to select the effective variables from the full-spectrum (FS) which have 675 spectra *** eleven variables were *** variables based LS-SVM model obtain 100% determination correct *** was proved that SPA was an effective algorithm for spectra variable *** a conclusion,Vis-NIR spectroscopy could be used to determine the growing areas of Tremella fuciformis fast and accurately.
Infrared spectroscopy(IR) technique combined with multivariate statistical analysis methods is widely used in fundamental and applied chemical *** selection of variables is a key step in developing a successful multiv...
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Infrared spectroscopy(IR) technique combined with multivariate statistical analysis methods is widely used in fundamental and applied chemical *** selection of variables is a key step in developing a successful multivariate analysis *** this study, the suitability score combined with successive projections algorithm(SPA-S) was proposed for variable *** is firstly used to removal high collineraity variables and then the suitability scores is used for selection informative *** these selected variables, partial least-squares discriminant analysis(PLSDA) model is constructed to evaluate the performance of *** proposed approach is applied to analyze the near infrared spectroscopy of six tea varieties and Fourier transform infrared spectroscopy of different kinds of edible vegetable *** results of SPA-S(SPA-S-PLSDA) were compared to those obtained by the suitability scores(S-PLSDA), successive projections algorithm(SPA-PLSDA)and PLSDA with all *** comparison demonstrated that SPA-S-PLSDA can perform good prediction performance and is an efficient tool for mining high dimension data.
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