About Dataset

Article Download (PDF): https://dergipark.org.tr/tr/download/article-file/1227592

ABSTRACT: In this study, machine vision system was developed in order to distinguish between two different variety of raisins (Kecimen and Besni) grown in Turkey. Firstly, a total of 900 pieces raisin grains were obtained, from an equal number of both varieties. These images were subjected to various preprocessing steps and 7 morphological feature extraction operations were performed using image processing techniques. In addition, minimum, mean, maximum and standard deviation statistical information was calculated for each feature. The distributions of both raisin varieties on the features were examined and these distributions were shown on the graphs. Later, models were created using LR, MLP, and SVM machine learning techniques and performance measurements were performed. The classification achieved 85.22% with LR, 86.33% with MLP and 86.44% with the highest classification accuracy obtained in the study with SVM. Considering the number of data available, it is possible to say that the study was successful.

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** CINAR I., KOKLU M. and TASDEMIR S., (2020), “Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods”, Gazi Journal of Engineering Sciences, vol. 6, no. 3, pp. 200-209, December, 2020.

DOI: https://doi.org/10.30855/gmbd.2020.03.03

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