{"url":"/dataset/rice-dataset-commeo-and-osmancik","name":"Rice Dataset Commeo and Osmancik","full_name":"Rice Dataset Commeo and Osmancik","description_markdown":"ata Set Name: Rice Dataset (Commeo and Osmancik)\r\nAbstract: 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.\t\r\n\r\nSource:\r\nIlkay CINAR\r\nGraduate School of Natural and Applied Sciences, \r\nSelcuk University, Konya, TURKEY\r\nilkay_cinar@hotmail.com\r\n\r\nMurat KOKLU\r\nFaculty of Technology, \r\nSelcuk University, Konya, TURKEY.\r\nmkoklu@selcuk.edu.tr\r\n\r\nhttps://www.kaggle.com/mkoklu42\r\nDATASET: https://www.muratkoklu.com/datasets/\r\n\r\nRelevant 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.\r\n\r\nAttribute Information:\r\n1. Area: Returns the number of pixels within the boundaries of the rice grain.\r\n2. Perimeter: Calculates the circumference by calculating the distance between pixels around the boundaries of the rice grain.\r\n3. Major Axis Length: The longest line that can be drawn on the rice grain, i.e. the main axis distance, gives.\r\n4. Minor Axis Length: The shortest line that can be drawn on the rice grain, i.e. the small axis distance, gives.\r\n5. Eccentricity: It measures how round the ellipse, which has the same moments as the rice grain, is.\r\n6. Convex Area: Returns the pixel count of the smallest convex shell of the region formed by the rice grain.\r\n7. Extent: Returns the ratio of the region formed by the rice grain to the bounding box pixels\r\n8. Class: Commeo and Osmancik.\r\n\r\nRelevant Papers / Citation Requests / Acknowledgements:\r\nCinar, 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.","description_withheld":null,"homepage":"https://www.muratkoklu.com/datasets/","introduced_date":"2022-02-11","introduced_date_note":null,"introduced_by":null,"license":{"name":"Public","url":"https://www.muratkoklu.com/datasets/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"},{"name":"Tables","url":"/datasets/modality/tables"}],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Rice Dataset Commeo and Osmancik"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}