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Figure 1. 

Chinese fir seedlings from different drought treatments.

Figure 2. 

Hyperspectral imaging system.

Figure 3. 

Flow chart of hyperspectral data extraction from NIR hyperspectral images.

Figure 4. 

ANNs structure.

Figure 5. 

Measured (a) LCC and (b) LWC in Chinese fir seedlings of the five drought treatment groups.

Figure 6. 

(a) Raw reflectance curves and (b) average reflectance curves of Chinese fir seedlings with different LCC and LWC.

Figure 7. 

Comparison of different preprocessing methods for hyperspectral data. (a) Hyperspectral data preprocessed by SG. (b) Hyperspectral data preprocessed by MSC. (c) Hyperspectral data preprocessed by SNV.

Figure 8. 

Result of applying SPA wavelength selection on the SG pre-processed spectrum for predicting LCC and LWC. (a) Variation of RMSE vs the number of wavelengths, and (b) the selected wavelengths for LCC prediction. (c) Variation of RMSE vs the number of wavelengths, and (d) the selected wavelengths for LWC prediction.

Figure 9. 

Process of extracting characteristic wavelength by CARS. (a) Number of preferred characteristic wavelength variables, (b) the root mean square error of cross-validation variation, and (c) regression coefficient path map for LCC. (d) Number of preferred characteristic wavelength variables, (e) the root mean square error of cross-validation variation, and (f) regression coefficient path map for LWC.

Figure 10. 

Correlation analysis between true and prediction values. (a) Prediction accuracy of SPA-ANNs model for LWC; (b) Prediction accuracy of CARS-ANNs model for LCC.