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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.

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:

1533831


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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