Akademik Veri Yönetim Sistemi
JOURNAL OF FOOD PROCESS ENGINEERING, cilt.48, sa.7, 2025 (SCI-Expanded)
The accurate classification of hazelnut cultivars is critical for ensuring product consistency, quality control, and market competitiveness in the food industry. Conventional identification methods remain manual, time-consuming, and error-prone, highlighting the need for automated alternatives. This study presents a novel, real-time machine vision system for classifying 11 hazelnut cultivars using a single side-view image. The proposed approach integrates three complementary feature extraction techniques: Elliptical Fourier Analysis (EFA) for contour and shape decomposition, circular masking for curvature quantification, and brown color gradient analysis for surface tone assessment. The extracted features-fully normalized and dimensionless to account for variations in imaging angle, distance, and nut positioning-were classified using three machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM-RBF), Multilayer Perceptron (MLP), and Extreme Learning Machine (ELM-RBF). Among the classifiers, SVM-RBF achieved the highest performance with an F1-score of 0.92 for multi-view images and 0.89 for side-view only. MLP and ELM-RBF followed with competitive yet slightly lower scores. The system demonstrated high robustness, computational efficiency, and interpretability. Overall, the proposed method offers a lightweight, scalable, and non-destructive solution for hazelnut cultivar classification and demonstrates strong potential for real-time deployment in industrial sorting lines and embedded systems in precision agriculture.