Antep Fıstığı Türlerinin Metasezgisel Optimizasyon ile Sınıflandırılması: Karşılaştırmalı Bir Çalışma

Ali Özarslan

doi:10.24925/turjaf.v14i9.2516-2527.9004

Authors

Ali Özarslan Hacettepe University https://orcid.org/0000-0002-9407-1787

DOI:

https://doi.org/10.24925/turjaf.v14i9.2516-2527.9004

Keywords:

Pistachio , Machine learning, Metaheuristic optimization, Hyperparameter optimization, Classification

Abstract

This study addresses the classification of two commercially important pistachio varieties, Kirmizi and Siirt, cultivated extensively in Turkey. The objective is to evaluate the effectiveness of metaheuristic optimization for improving machine learning classifier performance in agricultural product discrimination. A dataset of 2148 images was used to extract 28 morphological, color, and texture features. Class imbalance was mitigated using the Synthetic Minority Over-sampling Technique. Logistic Regression, Support Vector Machine (SVM), and XGBoost classifiers were applied. Hyperparameters of these classifiers were optimized using six metaheuristic algorithms: Particle Swarm Optimization, Artificial Bee Colony, Swift Flight Optimizer, Tardigrade Optimization Algorithm, Bilby Optimization Algorithm (BOA), and Schrödinger Optimizer (SCO). Models were evaluated using 5-fold cross-validation on the training set and tested on an independent test set. Results indicate that BOA combined with SVM achieved the highest classification accuracy. The best discriminative power, measured by the area under the ROC curve, was obtained with SCO combined with XGBoost. Compared to default parameter configurations, metaheuristic optimization led to notable improvements in both SVM and XGBoost classifiers. The findings demonstrate that metaheuristic-based hyperparameter optimization provides an effective and competitive approach for automatic pistachio variety discrimination, offering practical potential for deployment in agricultural sorting and quality control systems.

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