About Dataset

Data Set Name: Rice Dataset (Commeo and Osmancik)
Abstract: A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.

Source:
Ilkay CINAR
Graduate School of Natural and Applied Sciences,
Selcuk University, Konya, TURKEY
ilkay_cinar@hotmail.com

Murat KOKLU
Faculty of Technology,
Selcuk University, Konya, TURKEY.
mkoklu@selcuk.edu.tr

Relevant Information: In order to classify the rice varieties (Cammeo and Osmancik) used, preliminary processing was applied to the pictures obtained with computer vision system and a total of 3810 rice grains were obtained. Furthermore, 7 morphological features have been inferred for each grain. A data set has been created for the properties obtained.

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 region formed by the rice grain to the bounding box pixels
8. Class: Commeo and Osmancik.

Relevant Papers / Citation Requests / Acknowledgements:
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. https://doi.org/10.18201/ijisae.2019355381.

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** KOKLU, M. and OZKAN, I.A., (2020), “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.” Computers and Electronics in Agriculture, 174, 105507.

DOI: https://doi.org/10.1016/j.compag.2020.105507

UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset