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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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Atıf Yapan Yayın
Classification of almond kernels with optuna hyper-parameter optimization using machine learning methods
Scopus
Havuzumuzda 2 atıf almış
Almonds are an agricultural product with nutritious features. Almond species are important in terms of marketing and in terms of sustainability. Traditional almond classification methods can be time-consuming and expensive. In addition, Machine Learning (ML) methods stand out due to the tendency of human errors in the classification. ML methods can give both faster and more accurate results. This study aims to improve the performance of ML methods in the classification of almonds. In the study, almond data obtained from the data set were first processed with image processing methods. In this process, the noise and shadows in the images were removed. Then, 26 different features were extracted from the almond images. These extracted features were processed using SVM, RF, FCNN, LightGBM, CatBoost, and XGBoost ML methods in the classification process. It has provided accuracy, especially in SVM, FCCN, and XGBOOST models, and high values in metrics such as F1-Score. In the OptHO-SVM method, where Optuna was applied, the accuracy increased from 90.06 to 96.53%, while in the OptHO-FCNN method, the accuracy increased from 94.23 to 96.40%. In addition, significant improvements were observed in loss metrics such as Log Loss; Log Loss value improved by 58.75% in the OptHO-SVM method.
Atıf Yapan Makale Bilgileri
Kurumlar (3)
Afyon Kocatepe Üniversitesi
Afyonkarahisar, Turkey
Republic of Türkiye Ministry of National Education
Konya, Turkey
Selçuk Üniversitesi
Selçuklu, Turkey