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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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Performance analysis of deep feature extraction, feature fusion and feature selection with machine learning techniques in classification of chickpea seeds
Scopus
Havuzumuzda Open Access
Chickpeas are an essential food source rich in carbohydrates and proteins. However, distinguishing among different chickpea varieties remains challenging due to their high morphological similarity. Most recent studies rely on handcrafted or traditional feature-based methods, which are time-consuming, sometimes destructive, and often lack generalization capability. To overcome these limitations, we present a non-destructive computer vision and machine learning framework for efficient chickpea seed classification. Deep features were extracted using convolutional neural networks, fused to capture complementary representations, and reduced through the Tree–Seed Algorithm (TSA), a metaheuristic optimization method for feature selection. The selected features were classified using various machine learning algorithms, achieving a maximum accuracy of 95.6%. TSA reduced the feature dimensionality by approximately 60%, significantly decreasing training time while preserving high accuracy. Compared with existing studies reporting accuracies between 83% and 94%, the proposed approach improves classification performance by up to 12%. To the best of our knowledge, this is the first study to integrate deep feature fusion with TSA-based feature selection for chickpea seed classification. The results demonstrate a robust, efficient, and non-destructive alternative to conventional approaches.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey