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Raisin Dataset Data Set
Download: Data Folder, Data Set Description
Abstract: Images of the Kecimen and Besni raisin varieties were obtained with CVS. A total of 900 raisins were used, including 450 from both varieties, and 7 morphological features were extracted.
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
900 |
Area: |
Life |
Attribute Characteristics: |
Integer, Real |
Number of Attributes: |
8 |
Date Donated |
2021-04-01 |
Associated Tasks: |
Classification |
Missing Values? |
N/A |
Number of Web Hits: |
1558897 |
Source:
Ilkay CINAR
Faculty of Technology,
Selcuk University, Konya, TURKEY.
ORCID ID : 0000-0003-0611-3316
ilkay.cinar '@' selcuk.edu.tr
Murat KOKLU
Faculty of Technology,
Selcuk University, Konya, TURKEY.
ORCID ID : 0000-0002-2737-2360
mkoklu '@' selcuk.edu.tr
Sakir TASDEMIR
Faculty of Technology,
Selcuk University, Konya, TURKEY.
ORCID ID : 0000-0002-2433-246X
stasdemir '@' selcuk.edu.tr
Data Set Information:
Images of Kecimen and Besni raisin varieties grown in Turkey were obtained with CVS. A total of 900 raisin grains were used, including 450 pieces from both varieties. These images were subjected to various stages of pre-processing and 7 morphological features were extracted. These features have been classified using three different artificial intelligence techniques.
Attribute Information:
1.) Area: Gives the number of pixels within the boundaries of the raisin.
2.) Perimeter: It measures the environment by calculating the distance between the boundaries of the raisin and the pixels around it.
3.) MajorAxisLength: Gives the length of the main axis, which is the longest line that can be drawn on the raisin.
4.) MinorAxisLength: Gives the length of the small axis, which is the shortest line that can be drawn on the raisin.
5.) Eccentricity: It gives a measure of the eccentricity of the ellipse, which has the same moments as raisins.
6.) ConvexArea: Gives the number of pixels of the smallest convex shell of the region formed by the raisin.
7.) Extent: Gives the ratio of the region formed by the raisin to the total pixels in the bounding box.
8.) Class: Kecimen and Besni raisin.
Relevant Papers:
CINAR I., KOKLU M. and TASDEMIR S., (2020), Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods. Gazi Journal of Engineering Sciences, vol. 6, no. 3, pp. 200-209, December, 2020. DOI: [Web Link]
Citation Request:
CINAR I., KOKLU M. and TASDEMIR S., (2020), Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods. Gazi Journal of Engineering Sciences, vol. 6, no. 3, pp. 200-209, December, 2020. DOI: [Web Link]
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