Donated on 9/28/2012

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.

Dataset Characteristics


Subject Area


Associated Tasks

Classification, Clustering

Feature Type


# Instances


# Features


Dataset Information

Additional 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.

Has Missing Values?


Introductory Paper

Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

By M. Charytanowicz, J. Niewczas, P. Kulczycki, Piotr A. Kowalski, Szymon Łukasik, Slawomir Zak. 2010

Published in Information Technologies in Biomedicine

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values

0 to 7 of 7

Additional Variable 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.

Papers Citing this Dataset

Noise Regularization for Conditional Density Estimation

By Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause. 2019

Published in ArXiv.

Statistical Inference Using Mean Shift Denoising

By Yunhua Xiang, Yen-Chi Chen. 2016

Published in

Diversity-Driven Widening of Hierarchical Agglomerative Clustering

By Alexander Fillbrunn, Michael Berthold. 2015

Published in IDA.

MBACT-Multiclass Bayesian Additive Classification Trees

By Bereket Kindo, Hao Wang, Edsel Pena. 2013

Published in

0 to 5 of 8


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8 citations


Magorzata Charytanowicz

Jerzy Niewczas

Piotr Kulczycki

Piotr Kowalski

Szymon Lukasik


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