Nondestructive Estimation of Hazelnut (Corylus avellana L.) Terminal Velocity and Drag Coefficient Based on Some Fruit Physical Properties Using Machine Learning Algorithms

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 R-2 (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 R-2 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.

Automated summary

Hazelnut culture originated in Turkey, which has the highest volume and area of production. Understanding the aerodynamic properties of hazelnuts is crucial for equipment and structure design in hazelnut operations. This study used various independent variables to estimate the terminal velocity and drag coefficient of hazelnuts, and machine learning methods, such as logistic regression, support vector regression, and artificial neural networks, were employed. The results showed that ANN, SVR, and LR were the most successful models in terms of R-2, MSE, and MAE metrics.