Ilkay CINAR
Graduate School of Natural and Applied Sciences,
Selcuk University,
TURKEY,
ORCID ID :  0000-0003-0611-3316
lkay_cinar '@' hotmail.com

Murat KOKLU
Faculty of Technology,
Selcuk University,
TURKEY.
ORCID ID :  0000-0002-2737-2360
mkoklu '@' selcuk.edu.tr

Data Set Information:

Among  the certified rice grown in TURKEY,  the  Osmancik species, which has a large planting area since 1997 and the Cammeo species grown since 2014 have been selected for the study.  When  looking  at  the  general  characteristics  of  Osmancik species, they have a wide, long, glassy and dull appearance.  When looking at the general characteristics of the Cammeo species, they have wide and long, glassy and dull in appearance.  A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.

Attribute Information:

1.) Area: Returns  the  number  of  pixels  within  the boundaries of the rice grain.
2.) Perimeter: Calculates the circumference by calculating  the  distance  between  pixels around the boundaries of the rice grain.
3.) Major Axis Length: The longest line that can be drawn on the rice  grain,  i.e.  the  main  axis  distance, gives.
4.) Minor Axis Length: The shortest line that can be drawn on the rice  grain,  i.e.  the  small  axis  distance, gives.
5.) Eccentricity: It measures how round the ellipse, which has  the  same  moments  as  the  rice  grain, is.
6.) Convex Area: Returns  the  pixel  count  of  the  smallest convex shell of the region formed by the rice grain.
7.) Extent: Returns the ratio of the regionformed by the rice grain to the bounding box pixels.
8.) Class: Cammeo and Osmancik rices

Relevant Papers:

Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering,  vol.7, no.3 (Sep. 2019), pp.188-194. ([Web link])

Citation Request:

Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering,  vol.7, no.3 (Sep. 2019), pp.188-194. ([Web link])