The investigation proposed a new algorithm to automatize the identification process of pests and insects disease of Citrus reticulata Blanco var. Ponkan, in which multi-fractal spectra of image hue were set as inputs of wavelet neural network model. In the new algorithm, image boundary of damage pattern of Ponkan was extracted with improved watershed algorithm, and discontinuous boundary was processed with boundary following, meanwhile over-segmentation region was merged and boundary was marked, at last, damage pattern image was generated. After the work above, firstly, hue range 0°~120° of damage pattern image was equally segmented into 4 regions to generate 4 binary images. And then these binary images were analyzed by multi-fractal method to calculate the widths and heights of multi-fractal spectra of scale invariance region. In the end, the widths and heights of multi-fractal spectra were set as the inputs of wavelet neural network model to identify the pest and insects disease of citrus fruit. Test results showed that the accurate rate of identification of 5 pests and insects disease is about 87%, which means that widths and heights of multi-fractal spectra are sufficient to characterize the damage pattern of citrus fruit, and this method is applicable in machine automatic recognition for pests and insects disease of citrus fruit.