Discrimination on Maturity of Plums Based on Hyperspectral Imaging Information
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Abstract:
In this paper, plum was regarded as the research object, and image informations of different mature degrees of plum fruit (unripe, ripe, mature and overmature) samples were collected based on hyperspectral image. Then the images were treated with median filtering denoising. RGB and HSV color image models were obtained from different mature degrees by Matlab software and average and standard deviation of different components were regarded as the color feature value. RGB, HSV and RGB-HSV color characteristic value were established to identify the plum maturity PLS, and the established models were predicted. The results showed that the accuracy rate of discriminated model based on RGB-HSV color feature value was better than that of RGB and HSV. The accuracy rate of immaturity, instrumental, mature and overmature plum reached 98.35, 90.00%, 85.85% and 90.85%, respectively. The results showed that this discriminated model not only simplified but also enhances the discriminated ability of models, and provided the theoretical basis for the nondestructive detection and discrimination of plum maturity.
