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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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Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms
Scopus
Havuzumuzda 28 atıf almış
Of the millions of mushroom species growing all around the world, one type is edible, while the other is poisonous. It is not easy to distinguish edible and poisonous mushrooms from each other and it is a condition that requires expertise. The classification of poisonous and edible mushrooms is therefore important. Machine learning algorithms are an alternative method for classifying poisonous and edible mushrooms using morphological or physical features of fungi. The dataset used in this study is the Mushroom dataset available in the UC Irvine Machine Learning Repository. Based on 22 features in the Mushroom dataset and four different machine learning algorithms, models have been created for the classification of edible and poisonous fungi. The classification success rates of these models were obtained from Naive Bayes, Decision Tree, Support Vector Machine and AdaBoost algorithms with 90.99%, 98.82%, 99.98% and 100%, respectively. When these results were examined, taking into account the physical appearance features of the mushrooms, it was determined whether the mushrooms were edible and poisonous by 100% with the AdaBoost model.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey