The aboveground biomass (AGB) is a key index for predicting wheat yield. In the case of high biomass, the AGB estimation of single spectral feature or image texture is poor. Therefore, this study evaluated the ability of fusion of spectral reflectance and texture to predict wheat AGB. Among them the reflectance spectrum of the wheat canopy was collected by near-earth spectrometer, and the texture features of three bands of RGB were extracted by gray co-occurrence matrix. Partial least squares regression (PLS) model was used to evaluate the relationship between fusion features and AGB. The experimental results based on the validated data set show that the AGB estimation effect of feature fusion is better than that of single feature (R2 = 0.70; RMSE = 0.06). This shows that the combination of spectral reflectance and texture can improve the accuracy of AGB estimation in the later stage.
In order to improve and protect the water quality of Shuimentang Reservoir, the six relevant indexes of 12 sampling sites were measured, the spatial distribution characteristics of each index were analyzed, the correlation among each index was discussed, and the regression model was established for some indexes with strong correlation. Through the water quality detection and correlation analysis of Shuimentang Reservoir, Hydrogen Potential (pH) and Electrical Conductivity (EC) are significantly negatively correlated, Suspended Solids (SS) and Particulate Organic Matter (POM) are significantly positively correlated, and the correlation coefficients are -0.883 and 0.860, respectively. Based on the correlation analysis results, the "pH-EC" and "SS-POM" linear regression models were further constructed, and R-Square, Mean Square Regression and Sum of Squares of Error were 0.78, 1.20, 0.34 and 0.74, 32.83, 11.57, respectively. The two models have good fitting results, and pH and SS concentrations in Shuimengtang Reservoir water body were predicted by EC and POM concentrations, which reduces the number of water quality testing indicators and the testing cost.
As a novel and ultrasensitive detection technology that had advantages of fingerprint effect, high speed and
low cost, surface-enhanced Raman scattering (SERS) was used to develop the regression models for the fast quantitative
detection of thiram by support vector machine regression (SVR) in the paper. Meanwhile, three parameter optimization
methods, which were grid search (GS), genetic algorithm (GA) and particle swarm optimization (PSO), were employed
to optimize the internal parameters of SVR. Furthermore, the influence of the spectral number, spectral wavenumber
range and principal component analysis (PCA) on the quantitative detection was also discussed. Firstly, the experiments
demonstrate the proposed method can realize the fast and quantitative detection of thiram, and the best result is obtained
by GS-SVR with the spectra of the range of characteristic peak which are processed by PCA. And the effect of GS, GA,
PSO on the parameter optimization is similar, but the analysis time has a great difference in which GS is the fastest.
Considering the analysis accuracy and time simultaneously, the spectral number of samples over each concentration
should be set to 50. Then, developing the quantitative model with the spectra of range of characteristic peak can reduce
analysis time on the promise of ensuring the detection accuracy. Additionally, PCA can further reduce the detection error
through reserving the main information of the spectra data and eliminating the noise.
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