Seeds
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
Multivariate
Subject Area
Biology
Associated Tasks
Classification, Clustering
Feature Type
Real
# Instances
210
# 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?
No
Introductory Paper
By M. Charytanowicz, J. Niewczas, P. Kulczycki, Piotr A. Kowalski, Szymon Łukasik, Slawomir Zak. 2010
Published in Information Technologies in Biomedicine
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
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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
By Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause. 2019
Published in ArXiv.
By Alexander Fillbrunn, Michael Berthold. 2015
Published in IDA.
By Min Wei, Tommy Chow, Rosa Chan. 2015
Published in Entropy.
By Bereket Kindo, Hao Wang, Edsel Pena. 2013
Published in
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset seeds = fetch_ucirepo(id=236) # data (as pandas dataframes) X = seeds.data.features y = seeds.data.targets # metadata print(seeds.metadata) # variable information print(seeds.variables)
Charytanowicz,Magorzata, Niewczas,Jerzy, Kulczycki,Piotr, Kowalski,Piotr, and Lukasik,Szymon. (2012). Seeds. UCI Machine Learning Repository. https://doi.org/10.24432/C5H30K.
@misc{misc_seeds_236,
author = {Charytanowicz,Magorzata, Niewczas,Jerzy, Kulczycki,Piotr, Kowalski,Piotr, and Lukasik,Szymon},
title = {{Seeds}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5H30K}
}Creators
Magorzata Charytanowicz
Jerzy Niewczas
Piotr Kulczycki
Piotr Kowalski
Szymon Lukasik
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.