Identification of Rice Varieties Using Machine Learning Algorithms

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dc.contributor.authorÇınar, İlkay
dc.contributor.authorKoklu, Murat
dc.contributor.departmentOthertr_TR
dc.contributor.facultyOthertr_TR
dc.date.accessioned2022-09-13T08:54:46Z
dc.date.available2022-09-13T08:54:46Z
dc.date.issued2022
dc.description.abstractRice, which has the highest production and consumption rates worldwide, is among the main nutrients in terms of being economical and nutritious in our country as well. Rice goes through some stages of production from the field to the dinner tables. The cleaning phase is the separation of rice from unwanted materials. During the classification phase, solid ones and broken ones are separated and calibration operations are performed. Finally, in the process of extraction based on color features, the striped and stained ones other than the whiteness on the surface of the rice grain are separated. In this paper, five different varieties of rice belonging to the same trademark were selected to carry out classification operations using morphological, shape and color features. A total of 75,000 rice grain images, including 15,000 for each varieties, were obtained. The images were pre-processed using MATLAB software and prepared for feature extraction. Using a combination of 12 morphological, 4 shape features and 90 color features obtained from five different color spaces, a total of 106 features were extracted from the images. For classification, models were created with algorithms using machine learning techniques of knearest neighbor, decision tree, logistic regression, multilayer perceptron, random forest and support vector machines. With these models, performance measurement values were obtained for feature sets of 12, 16, 90 and 106. Among the models, the success of the algorithms with the highest average classification accuracy was achieved 97.99% with random forest for morphological features. 98.04% were obtained with random forest for morphological and shape features. It was achieved with logistic regression as 99.25% for color features. Finally, 99.91% was obtained with multilayer perceptron for morphological, shape and color features. When the results are examined, it is observed that with the addition of each new feature, the success of classification increases. Based on the performance measurement values obtained, it is possible to say that the study achieved success in classifying rice varieties.tr_TR
dc.description.indexWostr_TR
dc.description.indexScopustr_TR
dc.identifier.endpage325tr_TR
dc.identifier.issn/e-issn2148-9297
dc.identifier.issue2tr_TR
dc.identifier.startpage307tr_TR
dc.identifier.urihttps://doi.org/ 10.15832/ankutbd.862482tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/83991
dc.identifier.volume28tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesitr_TR
dc.relation.isversionof10.15832/ankutbd.862482tr_TR
dc.relation.journalAnkara Üniversitesi Ziraat Fakültesi Tarım Bilimleri Dergisitr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıtr_TR
dc.subjectColor featurestr_TR
dc.titleIdentification of Rice Varieties Using Machine Learning Algorithmstr_TR
dc.typeArticletr_TR

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Cilt:28 Sayı:02 (2022)