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seeds Data Set
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Abstract: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.

Data Set Characteristics:  

Multivariate

Number of Instances:

210

Area:

Life

Attribute Characteristics:

Real

Number of Attributes:

7

Date Donated

2012-09-29

Associated Tasks:

Classification, Clustering

Missing Values?

N/A

Number of Web Hits:

42027


Source:

Małgorzata Charytanowicz, Jerzy Niewczas
Institute of Mathematics and Computer Science,
The John Paul II Catholic University of Lublin, Konstantynów 1 H,
PL 20-708 Lublin, Poland
e-mail: {mchmat,jniewczas}@kul.lublin.pl

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
and
Systems Research Institute, Polish Academy of Sciences, Newelska 6,
PL 01-447 Warsaw, Poland
e-mail: {kulczycki,pakowal,slukasik,slzak}@ibspan.waw.pl


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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