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三种不同小麦品种的籽粒数据集 使用软X射线检测内核内部结构的高质量可视化

三种不同小麦品种的籽粒数据集 使用软X射线检测内核内部结构的高质量可视化

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Ma??gorzata Charytanowicz, Jerzy NiewczasInstitute of Mathematics and Computer Science,The John Paul II Catholic Univers......

数据结构 ? 9.1K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    Ma??gorzata Charytanowicz, Jerzy Niewczas
    Institute of Mathematics and Computer Science,
    The John Paul II Catholic University of Lublin, Konstantyn?3w 1 H,
    PL 20-708 Lublin, Poland
    e-mail: {mchmat,jniewczas}

    Piotr Kulczycki, Piotr A. Kowalski, Szymon Lukasik, Slawomir Zak
    Department of Automatic Control and Information Technology,
    Cracow University of Technology, Warszawska 24, PL 31-155 Cracow, Poland
    Systems Research Institute, Polish Academy of Sciences, Newelska 6,
    PL 01-447 Warsaw, Poland
    e-mail: {kulczycki,pakowal,slukasik,slzak}

    Data Set Information:

    The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for
    the experiment. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates. Studies were conducted using combine harvested wheat grain originating from experimental fields, explored at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin.

    The data set can be used for the tasks of classification and cluster analysis.

    Attribute Information:

    To construct the data, seven geometric parameters of wheat kernels were measured:
    1. area A,
    2. perimeter P,
    3. compactness C = 4*pi*A/P^2,
    4. length of kernel,
    5. width of kernel,
    6. asymmetry coefficient
    7. length of kernel groove.
    All of these parameters were real-valued continuous.

    Relevant Papers:

    M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24.

    Citation Request:

    Contributors gratefully acknowledge support of their work by the Institute of Agrophysics of the Polish Academy of Sciences in Lublin.




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