Simultaneous Measurement of Chlorophyll and Water Content in Navel Orange Leaves Based on Hyperspectral Imaging

摘要

Chlorophyll and leaf water content are important nutritional indicators of the Gannan navel orange tree. Hyperspectral imaging was used to simultaneously estimate chlorophyll and water content of Gannan navel orange leaves. The genetic algorithm (GA) and successive projections algorithm (SPA) were applied in selecting characteristic wavelengths after average spectra were extracted from the region of interest (ROI). The hyperspectral effective information was extracted using the partial least squares (PLS) model. The results derived from these techniques were then compared. The prediction model of chlorophyll and water content built using SPA on 8 and 14 selected wavelengths (3.15% and 5.15% of the total number of wavelengths) exhibited higher coefficients of determination r of 0.98 and 0.93 and root mean square error of prediction (RMSEP) values of 3.40 and 0.04, respectively. The results show that the accuracy of the quantitative analysis conducted by hyperspectral imaging can be improved through appropriate wavelength selection. Although the RMSEP value of SPA-PLS was slightly higher than that of GA-PLS, the SPA-PLS model can be used for applications because it was simpler and easier to interpret.