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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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Determination of wheat types using optimized extreme learning machine with metaheuristic algorithms
Scopus
Havuzumuzda 16 atıf almış
In order to increase the market value and quality of wheat, it is important to separate different types and determine the amount of foreign matter using the visual properties of durum and bread wheat. In this study, the extreme learning machine (ELM) algorithm, which is often preferred in real-time applications, was used to make classifications using features obtained from images containing the wheat kernel and foreign matter. The feature selection process was applied to remove the irrelevant ones from the obtained 236 features. In addition, the Harris hawks’ optimizer (HHO), a novel method in the literature, and the particle swarm optimizer (PSO), one of the well-known algorithms, were used to improve the ELM model. As part of this study, new models called HHO-ELM and PSO-ELM were created and compared with the original ELM model and other artificial neural networks (ANNs) studies published in the literature. As a result, in comparison with other models, the optimized ELM models demonstrated good stability and accuracy, having 99.32% in binary classification and 95.95% in multi-class classification.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey