Non-destructive prediction of hazelnut and hazelnut kernel deformation energy using machine learning techniques

Abstract

ABSTRACT The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role in the processing of hazelnut and hazelnut kernels, quality assessment, and the development of harvesting and post-harvest technologies. The data used in the data set was determined by applying compression tests and artificial neural networks, support vector regression, and multiple linear regression methods were applied to the data obtained. The aim of the study ws to determine the deformation energy of hazelnuts and hazelnut kernels based on some mechanical properties of hazelnuts using nondestructive machine learning methods instead of traditional measurement methods with minimum error, minimum labor, and in the shortest time. The average R2 for kernels and hazelnuts was ANN 95.2%, SVR 89.6%, and MLR 86.1%. The average MSE for kernels and hazelnuts was ANN 0.006, SVR 0.012, and MLR 0.072. The machine learning methods used in the study provided results close to the ideal statistical metrics. According to the analyses of the machine learning methods, results similar to the optimal statistical metrics were obtained. The most successful and least-error methods were the artificial neural network, support vector regression and multiple linear regression, respectively.