Abstract
Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis.
 Area of study: Samsun Province, Turkey.
 Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties.
 Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties.
 Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models.
Highlights
Hazelnut (Corylus avellana L.) is a very healthy nutrient for humans and animals since it contains fatty acids, vitamins, proteins and minerals
This study aims to classify hazelnut varieties with Artificial neural networks (ANN) and discriminant analysis using physical, mechanical, and optical properties
The data used in the study were normalized between 0 and 1 (Purushothaman & Srinivasa, 1994); 80% of the data were used for training, while 10% were used for test and 10% were used for validation to develop ANN and discriminant models
Summary
Hazelnut (Corylus avellana L.) is a very healthy nutrient for humans and animals since it contains fatty acids, vitamins, proteins and minerals. The physical properties of agricultural materials are important parameters used to design agricultural and food processing machines such as crop processing, handling, sieving, storing and drying machines and equipment Their physical properties, mechanical properties and optical properties are used for classification of varieties (Mohsenin, 1970; Tabatabaeefar, 2003). ANN is extremely efficient and successful in studies with non-linear data For this reason, ANN has a very important potential in the classification of agricultural products (Visen et al, 2002; Dubey et al, 2006; Guiné et al, 2015). This study aims to classify hazelnut varieties with ANNs and discriminant analysis using physical, mechanical, and optical properties. Physical, mechanical and optical properties of hazelnut varieties were determined for each of three major axes (X, Y, Z) (Fig. 2). Physical properties include geometric mean diameter, sphericity, grain volume, surface area, shell thickness and grain weight; mechanical
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