## 1 Introduction
They achieve an average classification success of 85.28%, 85.19%, and 83.32% for ResNet50, ResNet152, and VGG16 models, respectively. In [16], Kirmizi and Siirt pistachio images are used for classification in a CNN model based on VGG16. The VGG16 model achieves 99.91% classification accuracy. In [17], Kirmizi and Siirt pistachio types are classified using CNNs of AlexNet, VGG16, and VGG19 models. Among these, the VGG16 model achieves the highest accuracy with 98.84%. The results show that pistachio species can be successfully used for computer vision. In [18], Kirmizi and Siirt pistachio are classified with 2148 images using ResNet architecture. The highest classification accuracy is obtained as 88.58% in fold-1, and the 5-fold average classification accuracy is 86.16%. In [19], a setup using a dual camera setup and a Cartesian manipulator is constructed to distinguish between open and closed shelled pistachios. Pistachios in this setup are detected with a deep learning-based object detection algorithm. Thus, this method provides 98% and 85% successful detection for open and closed shelled pistachios, respectively. In [20], the different frequency components generated by the impact of pistachios on a surface are used in a high sensitivity carbon microphone and a high-level programming language to distinguish between open and closed shelled pistachios. They propose an acoustic emission-based system consisting of mel frequency cepstral coefficients (MFCC) for feature extraction and the SVM method for classification, which they believe will improve the product quality and processing efficiency of pistachios. In [21], they have created a computer vision system to generate images containing 2148 high-resolution pistachio species and classify them. The resulting dataset is used in an advanced multi-stage model using principal component analysis (PCA) and k-NN technique, and a classification success of 94.18% is found for the 16 features obtained. In [22], an unsupervised learning-based method is introduced to identify the opening of pistachio shell, which uses clustering algorithms and ResNet18 CNN to categorize pistachio features. A 99.31% accuracy in three-dimensional space has been achieved by the method, which can be applied to both qualitative and quantitative analysis of pistachio opening. Significant potential for enhancing pistachio quality detection, product classification, and reducing resource consumption is offered by this approach, providing valuable insights for future intelligent sorting equipment and quality detection of other agricultural products. As a result, there is a need for new techniques and technologies for the categorization and labeling of pistachios. Therefore, this research aims to categorize Kirmizi and Siirt pistachios using deep learning-based classification methods for pistachio species, incorporating k-fold cross-validation. The significant contributions of this study are as follows:  
* Since the quality of shelled pistachios is crucial for the economy, export, and marketing efforts according to their type, Kirmizi and Siirt pistachios have been classified using various deep learning-based classification models with k-fold cross-validation. A dataset of 2148 images, consisting of 1232 Kirmizi and 916 Siirt pistachio images, has been used for this classification. * Seven convolutional neural network (CNN) models, trained through transfer learning, have been utilized for classification, along with the proposed model, MSU-CNN, which also incorporates k-fold cross-validation. * The k-fold cross-validation technique has been utilized to enhance the generalization ability of the classification model, prevent overfitting, and improve performance reliability. * The classification performances have been evaluated not only based on classification accuracy but also using sensitivity, specificity, precision, F1-scores, and ROC-AUC values. The results show that the proposed approaches can properly identify pistachio species.