An effective wavelength utilization for spectroscopic analysis on orchid chlorophyll measurement

To insure the quality and quantity of yields, computational tools for monitoring and analyzing the growth of crops are of great importance in scientific agriculture. In recent years, non-destructive measurements that utilize spectroscopy for crop monitoring have drawn much attention, and algorithms for selecting proper wavelengths are worth being investigated, since they have deep impact on the accuracy. In this research, an approach for utilizing wavelengths on orchid chlorophyll prediction is proposed. The newly proposed method is based on the response surface methodology (RSM), and we apply it to four wavelength selection algorithms to see the effectiveness. The spectral data in our experiment is obtained by the interactance measurement on 600 orchid plants with a hand-held spectrometer, and the actual chlorophyll content is also measured with a CCI meter for verification. Experimental results show that this new approach significantly improves the utilization of wavelengths for building prediction model, raising R² from 88.74% to 93.95% and reducing the RMSECV from 7.5 to 6.94 for 15 wavelengths. Therefore, the proposed method is worth being applied to devising wavelength selection algorithms.