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柑橘成熟度多重分形無損檢測(cè)
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湖南省自然科學(xué)基金資助項(xiàng)目(07JJ6129)和湖南省農(nóng)業(yè)廳重點(diǎn)課題資助項(xiàng)目(2007004A)


Non-destructive Detection of Maturity Degree of Citrus Fruit Based on Multi-fractal
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    摘要:

    為無損檢測(cè)柑橘成熟度，將柑橘主要色調(diào)分布范圍30°~120°進(jìn)行了等分,，形成3幅色調(diào)圖,，分析每幅色調(diào)圖標(biāo)度不變域及多重分形譜，以多重分形譜高度和寬度表征柑橘果皮色澤特征,，并以此作為BP神經(jīng)網(wǎng)絡(luò)的輸入,，可溶性總固形物含量為輸出，建立柑橘成熟度模型,，映射柑橘成熟度,。試驗(yàn)的平均正確識(shí)別率為82%，表明通過柑橘果皮色調(diào)的多重分形譜能無損檢測(cè)柑橘成熟度,。

    Abstract:

    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. 

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曹樂平.柑橘成熟度多重分形無損檢測(cè)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(7):182-185. Cao Leping. Non-destructive Detection of Maturity Degree of Citrus Fruit Based on Multi-fractal[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(7):182-185.

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