Classification of raisin grains variety using some machine learning methods
Abstract
One of the agricultural crops with considerable nutritional and financial worth is raisins. Every year, the world produces and consumes millions of tons of raisins. In this work, machine learning was used to categorize two different raisin kinds that are grown in our nation. Machine learning techniques Decision Trees and Random Forest were used to classify the 2-class data set with 7 different attributes that were acquired as a ready-made data set. With 020 Random Forest and Decision Trees, classification accuracy was 85.44% and 85.22%, respectively, in the analyses that were conducted.
How to Cite
Unal, Y., Kaplan, H., Bektas, Y., & Caglar, M. B. (2023). Classification of raisin grains variety using some machine learning methods. New Trends in Computer Sciences, 1(1), 62–69. https://doi.org/10.3846/ntcs.2023.18015
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Yu, X., Liu, K., Wu, D., & He, Y., (2012). Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food Bioprocess Technology, 5(5), 1552–1563. https://doi.org/10.1007/s11947-011-0531-9
Chaudhuri, S., Fayyad, U., & Bernhardt, J. (1999). Scalable classification over SQL databases. In Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337) (pp. 470–479). IEEE. https://doi.org/10.1109/ICDE.1999.754963
Chein, C. F., & Chen, L. F. (2008) Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34, 280–290. https://doi.org/10.1016/j.eswa.2006.09.003
Cınar, I., Koklu, M., & Tasdemir, S. (2020). Classification of raisin grains using machine vision and artificial intelligence methods. Gazi Journal of Engineering Sciences, 6(3), 200–209. https://doi.org/10.30855/gmbd.2020.03.03
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1
Food and Agriculture Organization of the United Nations Statistics Division. (2022, July 14). Crops and livestock products. http://www.fao.org/faostat/en/#data/qc
Francis, F. J., & Clydesdale, F, M. (1975). Food colorimetry: Theory and applications. AVI Publishing, Westport.
Gupta, P. (2017, June 5). Cross-validation in machine learning. https://towardsdatascience.com/cross-validation-inmachine-learning-72924a69872f
Okamura, N. K., Delwiche, M. J., & Thompson, J. F. (1993). Raisin grading by machine vision. Transactions of the ASAE, 36(2), 485–492 https://doi.org/10.13031/2013.28363
Omid, M., Abbasgolipour, M., Keyhani, A., & Mohtasebi, S. S. (2010). Implementation of an efficient image processing algorithm for grading raisins. International Journal of Signal Image Processing, 1(1), 31–34.
Raisin Dataset. (2022). [Data set]. https://www.muratkoklu.com/datasets/
Rokach, L., & Maimon, O. (2008). Data mining with decision trees: Theory and applications. World Scientific. https://doi.org/10.1142/6604
Schaffer, C. (1993). Selecting a classification method by cross-validation. Machine Learning, 13(1), 135–143. https://doi.org/10.1007/BF00993106
Soylemezoglu, G., Kunter, B., Akkurt, M., Sağlam, M., Ünal, A., Buzrul, S., & Tahmaz, H. (2015). Viticulture development methods and production targets. In Turkish Agricultural Engineering 8th Technical Congress, Proceedings (pp. 606–629).
Yu, X., Liu, K., Wu, D., & He, Y., (2012). Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food Bioprocess Technology, 5(5), 1552–1563. https://doi.org/10.1007/s11947-011-0531-9