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Rice (Cammeo and Osmancik) Data Set
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Abstract: 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.

Data Set Characteristics:  

Multivariate

Number of Instances:

3810

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

8

Date Donated

2019-10-06

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

1202


Source:

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])


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