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Classification of rice varieties with deep learning methods
Scopus
Toplam 222 atıf DOI
Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.
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Classification of bread wheat genotypes by machine learning algorithms
Scopus
Havuzumuzda 24 atıf almış
Bread wheat, one of the staple food products, is a grain that forms the main ingredient of flour used in bakery products, especially in bread. Wheat has a large market in the world. The correct classification of bread wheat seeds is of great importance in order for the farmers to obtain an efficient harvest from bread wheat and to earn high income. In this study, a data set was created by taking 8354 images from certified 'Ayten Abla', 'Bayraktar 2000', 'Hamitbey', 'Şanlı' and 'Tosunbey' bread wheat varieties. Classification of wheat genotypes was carried out in 4 stages using images of bread wheat genotypes. In the first stage, 90 colors (C), 4 shapes (S) and 12 morphological (M) features were extracted from the images in this data set by image processing and feature selection method. The features obtained in the second stage were combined in different combinations. In the third stage, in the selection of the features that were effective in classification performance, feature selection was made from all the features combined with the Artificial Bee Colony (ABC) algorithm. Finally, bread wheat genotypes were classified by using these features, determined in three stages, as Support Vector Machines (SVM), Decision Tree (DT) and Quadratic Discriminant (QD) classifier which were machine learning algorithms. To make the classification process more accurate and objective, 10 fold cross validation was performed. The most successful classification process was obtained with SVM. The success rates obtained using 46, 94, 106, 102 and 90 features with SVM were 96.28 %, 95.81 %, 95.77 %, 95.66 % and 95.34 %, respectively.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey