rice_species_classification
Science Score: 31.0%
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Low similarity (1.8%) to scientific vocabulary
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Basic Info
- Host: GitHub
- Owner: GongJr0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 459 KB
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Created about 2 years ago
· Last pushed about 2 years ago
Metadata Files
Readme
Citation
README.md
Rice Grain Classification Using Ensemble Methods
Information and citations regarding the dataset used in this project can be found in ./Citation.txt.
Owner
- Name: Güney K.
- Login: GongJr0
- Kind: user
- Location: Warsaw
- Repositories: 1
- Profile: https://github.com/GongJr0
Full-Time Python Struggler
Citation (Citation.txt)
DATASET: https://www.muratkoklu.com/datasets/ 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 DATASET: https://www.muratkoklu.com/datasets/ 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.