## 1 Introduction  
Pistachios (_Pistacia vera_ L.), classified under the family Anacardiacaea, are widely recognized as a global delicacy, valued for their distinctive flavor and versatile applications [1, 2]. Pistachios are rich in protein, fiber, monounsaturated fats, various vitamins, minerals, phytochemicals, and polyphenols [3, 4, 5, 6]. Both epidemiological and clinical research on pistachios increasingly highlights the health benefits associated with the consumption of tree nuts [3, 6]. Pistachio is one of the agricultural products originating from the Middle East and Central Asia. The USA, Iran, Turkey, and Syria, the leading global producers of pistachios, account for nearly 90% of the total worldwide production. Pistachio is consumed globally, with Turkey ranking third in the world in pistachio production [7]. The consumer price of pistachios, as a high-cost agricultural product, is influenced by the product's quality. Therefore, assessing the quality of shelled pistachios is crucial for economic, marketing, and export purposes. Improved quality can result in improved consumption rates and enhanced marketing opportunities. Moreover, accurately assessing the quality of pistachios through efficient and straightforward smart system-based methodsis crucial for preventing economic losses in marketing and export [8; 9]. In conclusion, the demand for innovative techniques and technologies for pistachio shelling and classification continues to increase. Some studies conducted in the literature regarding pistachios are presented as follows. A smart classification system for pistachios is developed using an artificial neural network (ANN) in [8]. The ANN classifier is trained using acoustic signals generated by the impact of pistachios against a steel plate. Various signal processing techniques are used to extract the features. It has been reported that this method provided 99.89% classification accuracy. In [10], an approach that includes machine learning techniques and image processing is proposed to classify pistachios in shell. The ANN classifier achieves a success rate of 99.4%, while the Support Vector Machine (SVM) classifier achieves a success rate of 99.8%. In [11], various machine learning classifiers are used to detect pistachios with open shells, rotten pistachios, and pistachios with closed shells. First, the dataset is augmented using data augmentation, and feature extraction is performed with AlexNet and GoogleNet. Then, the most decisive 300 features are selected with PCA and classified with SVM. A maximum accuracy of 99% has been achieved by giving the features obtained from GoogleNet to the classifier. In [1], a deep learning-based method is proposed to detect spinach mixtures in pistachios. The method achieves more than 90% accuracy on 6-class dataset combinations using various color spaces and CNN architectures. The VGGNet-19 model achieves 100% accuracy in classifying the LAB color space. VGGNet-19 on HSV and ResNet-50 on YUV models achieve more than 98% accuracy in detecting pistachio adulteration. In [12], deep autoencoder neural networks are used to separate pistachios into defective and perfect pistachios. A classification success of 80.3% is achieved in detecting defective pistachios. In [13], a dataset of 958 pistachios with defective and perfect pistachios is used for classification. GoogleNet, VGG16, and ResNet models achieve a classification accuracy of 95.8%, 95.83%, and 97.2%, respectively. In [14], image features are extracted using deep neural network embeddings, and pistachio species are classified with small-scale machine learning algorithms trained on these feature vectors. The findings show that logistic regression applied to the features extracted from the second-to-last layer of the Painters network achieves an accuracy of 97.20%. In [15], a computer vision-based system is presented to recognize whether the shell of a pistachio is open or closed. Feature extraction and CNN-based classification are performed.