Abstract： Chlorophyll distribution in cucumber leaves was non-destructively and rapidly measured based on genetic algorithms (GA) combined with independent component analysis (ICA) by Hyper-spectral images technique. Spectrum was extracted from hyper-spectral images of cucumber leaves after preprocessing. Firstly GA was used to search for an optimum informative wavelength of chlorophyll, then ICA signals were computed to build multiple regression model (MLR). The results show that 280 wavelengths were selected by GA and MLR model which was obtained based on 8 ICA signals. MLR model performs well with prediction coefficients of 0.931 2 and root mean standard error of prediction (RMSEP) of 0.191 4. 8 ICA signals of every pixel in hyper-spectral images were computed and chlorophyll content of every pixel was obtained according to the MLR model. Finally, the chlorophyll distribution map was estimated. Overall results sufficiently demonstrate that the hyper-spectral imaging technique can be used to measure the chlorophyll concentration and to estimate the distribution of chlorophyll in cucumber leaf.
2011, Vol. 32 
