Atıf İçin Kopyala
Kabaş Ö., Kayakuş M., Moiceanu G.
FOODS, cilt.12, sa.15, ss.1-16, 2023 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
12
Sayı:
15
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Basım Tarihi:
2023
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Doi Numarası:
10.3390/foods12152879
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Dergi Adı:
FOODS
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
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Sayfa Sayıları:
ss.1-16
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Akdeniz Üniversitesi Adresli:
Evet
Özet
Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential that an understanding of hazelnuts’ aerodynamic properties, i.e., terminal velocity and drag coefficient, is acquired. In this study, the moisture, mass, density, projected area, surface area, and geometric diameter were used as independent variables in the data set, and the dependent variables terminal velocity and drag coefficient estimation were determined. In this study, logistic regression (LR), support vector regression (SVR), and artificial neural networks (ANNs) were used based on machine learning methods. When the results were evaluated according to R2 (determination coefficient), MSE (mean squared error), and MAE (mean absolute error) metrics, it was seen that the most successful models were the ANN, SVR, and LR, respectively. According to the R2 metric, the ANN method achieved 91.5% for the terminal velocity of hazelnuts and 85.9% for the drag coefficient of hazelnuts. Using the independent variables in the study, it was seen that the terminal velocity and drag coefficient value of hazelnuts could be successfully estimated.