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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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Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection
Scopus
Havuzumuzda 49 atıf almış
Coffee is an important export product of the tropical countries where it is grown. Therefore, the separation of coffee beans in the world in terms of the quality element and variety forgery is an important situation. Currently, the use of manual control methods leads to the fact that the parsing processes are inconsistent, time-consuming, and subjective. Automated systems are needed to eliminate such negative situations. The aim of this study is to classify 3 different coffee beans by using their images, through the transfer learning method by utilizing 4 different Convolutional Neural Networks-based models, which are SqueezeNet, Inception V3, VGG16, and VGG19. The dataset used in the models’ training was created specially for this study. A total of 1554 coffee bean images of Espresso, Kenya, and Starbucks Pike Place coffee types were collected with the created mechanism. Model training and model testing processes were carried out with the obtained images. In order to test the models, the cross-validation method was used. Classification success, Precision, Recall, and F-1 Score metrics were used for the detailed analysis of the models of performances. ROC curves were used for analyzing their distinctiveness. As a result of the tests, the average classification success of the models was determined as 87.3% for SqueezeNet, 81.4% for Inception V3, 78.2% for VGG16, and 72.5% for VGG19. These results demonstrate that the SqueezeNet is the most successful model. It is thought that this study may contribute to the subject of coffee beans of separation in the industry.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Amasya Üniversitesi
Amasya, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey