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

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.

KEYWORDS:

• SVR
• mechanical properties
• non-destructive
• machine learning
• MLR

Acknowledgement

This study was supported by the Scientific Research Fund of National University of Science and Technology Politehnica Bucharest, Bucharest, Romania and the Akdeniz University, Antalya, Turkey.

Abbreviation

AI	=	Normalized data
Ai	=	Value to be normalized.
ANN	=	Artificial Neural Networks
β0	=	Intercept point of the regression curve on the y-axis
β1	=	Coefficient of the first prediction variable X1
e	=	Error term
f(x)	=	Output vector
j	=	Value that makes AI less than 1
L	=	Length
LDPE	=	Low-density polyethylene
MLR	=	Multiple linear regression
Mm	=	Millimetre
MSE	=	Mean squared error.
N	=	Newton
p-value	=	Probability value
R2	=	Coefficient of determination
RBF	=	Radial basis function
ReLU	=	Rectified linear unit
SVM	=	Support vector machines
SVR	=	Support vector regression
T	=	Thickness
USA	=	United States of America
W	=	Width
X	=	Dependent variable
Y	=	Independent variable

Highlights

• Deformation energy estimation of hazelnut and kernel were carried out using machine learning methods.

• Moisture content, width, length, thickness, rupture force, deformation and rupture stress were used as independent variables in the data set.

• Three different machine learning methods were used.

• Statistical metrics R² and MSE were used to evaluate the performance of the methods.

• The best performing methods are artificial neural network, support vector regression and multiple linear regression, respectively.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the This research was funded by National University of Science and Technology Politehnica Bucharest through PubArt program.