基于高光谱成像分析的冬枣微观损伤识别
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高等学校博士学科点专项科研基金资助项目(20113227120014)、江苏高校优势学科建设工程资助项目(苏政办(2014)37号)和江苏大学高级专业人才科研启动基金资助项目(10JDG026)


Identification of Slight Bruises on Winter Jujube Based on Hyperspectral Imaging Technology
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    摘要:

    为减少微观损伤引起的储藏腐烂损失,延长冬枣的储藏期,提高冬枣的储藏效益,以山东沾化冬枣为研究对象,利用高光谱成像系统采集轻微损伤发生不到1h的冬枣损伤部位的高光谱图像,得到波长在871~1766nm范围内的256幅高光谱分量图像。结合无信息变量消除法及相关系数法进行特征波长筛选,剔除不敏感波段,选取了944、1035、1187、1376nm 4个特征波长。对以上4个特征波长对应的分量图像进行主成分分析,选择第1主成分图像作为待分割图像,对其进行灰度变换等图像预处理,并运用自适应阈值分割法对其进行图像分割,实现了轻微损伤区域的有效识别。对100个轻微损伤冬枣样本的识别试验结果表明,所提方法的正确识别率为98%。

    Abstract:

    In order to reduce storage losses, extend the storage period and improve the storage efficiency of winter jujubes, taking the winter jujubes in Zhanhua City as research object, a hyperspectral imaging system was built for detecting bruises happened less than 1h on ‘Zhanhua’ winter jujubes. The 256 hyperspectral images data within 871~1766nm wavelengths were obtained by the hyperspectral imaging system. The effective wavelengths were selected by using uninformative variables elimination (UVE) method and correlation coefficient method. Eventually, four characteristic wavelengths, 944, 1035, 1187 and 1376nm were selected. Four images were mapped to four characteristic wavelengths, the principal component analysis (PCA) was used based on the four images, and the first component of the image was selected to future process and segment. Many pretreatment methods were used for the first component of the image, such as gray level transformation and so on, and the method of adaptive threshold was applied to segment. Finally, the slightly damaged area could be obtained. Thus, the slight bruises on winter jujubes were recognized. Independent validation set of 100 bruised winter jujubes was used to evaluate the performance of the developed algorithm. Results showed that 98% of the bruised winter jujubes were recognized correctly.

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魏新华,吴 姝,范晓冬,黄嘉宝.基于高光谱成像分析的冬枣微观损伤识别[J].农业机械学报,2015,46(3):242-246.

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  • 收稿日期:2014-07-09
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  • 在线发布日期: 2015-03-10
  • 出版日期: 2015-03-10