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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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Light weight convolutional neural network and low-dimensional images transformation approach for classification of thermal images
Scopus
Havuzumuzda Open Access 26 atıf almış
Thermal energy is emitted in the infrared range between X-ray and Gamma rays, which are invisible to the human eye. Thermal cameras can detect the temperature that arises due to the heat emitted by the objects in a non-contact way and transform it into an image. These images ensure to detection of objects regardless of ambient occlusion. Based on this problem, five different classification models were proposed within the scope of the study. New low-dimensional images were obtained by extracting the features of thermal images with HOG (Histogram Oriented of Gradients), LBP (Local Binary Pattern), SIFT (Scale Invariant Feature Transform), and GF (Gabor Filter) methods. These images are classified by a CNN (Convolutional Neural Network) model called LW-CNN (Light Weight CNN). Raw thermal images were classified with the LW-CNN model without pre-processing. In order to analyze the efficiency of the proposed models, the results were compared via the pre-trained VGG16 model. Three different datasets containing thermal images were used in classification processes. The highest classification accuracy was obtained from the LW-CNN model in the performance evaluations carried out on the three datasets. With this model, the classification accuracies obtained from the datasets are 98.58%, 95.56%, and 100%, respectively.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey