Görüntü işleme teknikleri kullanılarak bazı meyvelerin sınıflandırılması
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Date
2020
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Eğitim Bilimleri Enstitüsü
Abstract
In this thesis, an image processing algorithm and classification unit were developed to classify the fruits according to their size and color characteristics. For this purpose, a total of 300 fruits (50 fruit samples from each of the Starkrimson Delicious and Golden Delicious apple varieties, Washington Navel and Valencia Midknight orange varieties, Ekmek and Eşme quince varieties) were used in the experiments. The size and color values measured with a caliper and a spectrophotometer were entered in the developed image processing algorithm to determine the success rates of classifying the fruits. The integration of image processing algorithm with the classification unit classified 88% of the Starkrimson Delicious variety, 100% of the Golden Delicious apple variety, 96% of the Washington Navel variety, and 82% of the Valencia Midknight variety successfully. In the quince classification process, taking into consideration that the classification was made according to the weights in TS 1817 standard, each quince was weighed on a precision scale and the relationship between dimension and weight was determined. The smallest and largest diameters for both quince varieties were determined, then the highest and lowest diameters of each fruit were entered in the algorithm for classification. The success rates of classification with this method were found to be 95% for Ekmek and 86% for Eşme quince varieties, respectively. For each color channel, when the upper and lower limit values from the spectrophotometer were entered in the algorithm, the classification success was found to be 100% for the apple varieties. On the other hand, the size and color values of fruits with image processing algorithm were also evaluated by using estimation techniques in data mining. For this purpose, K Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes classification and Multi-Layer Perceptron Neural Network (MLP) algorithms were used. The algorithms were run using 10-fold cross-validation method. In addition, Random Forest (RO) method was chosen from the meta learning algorithms. The successes of predicting the correct fruit class and color measurements in training and testing of artificial classifiers were 93.6% for KNN, 90.3% for DT, 88.3% for Naive Bayes, 92.6% for MLP and 94.3% for RO, respectively.
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meyve sınıflandırma, görüntü işleme, veri madenciliği