柑橘成熟度多重分形无损检测

摘要

Aimed at determining the maturity degree of citrus fruit non-destructively, three tone images were generated by dividing hue range 30° ~ 120° of fruit image equally to three parts. Scale invariance and spectra of multi-fractal were analyzed. Height and width of multi-fractal spectra were extracted as features of color and luster of pericarp of fruits, and were set as input of BP neural network. With total soluble solid contents as the output of network, the neural network maturity degree model mapped fruit image into degree of maturity. The correctness of inspection test was 82% , which showed that maturity degree of citrus fruit could be detected non-destructively based on multi-fractal spectra.%为无损检测柑橘成熟度,将柑橘主要色调分布范围30°～ 120°进行了等分,形成3幅色调图,分析每幅色调图标度不变域及多重分形谱,以多重分形谱高度和宽度表征柑橘果皮色泽特征,并以此作为BP神经网络的输入,可溶性总固形物含量为输出,建立柑橘成熟度模型,映射柑橘成熟度.试验的平均正确识别率为82％,表明通过柑橘果皮色调的多重分形谱能无损检测柑橘成熟度.