## 4 Results and Discussion
### Comparison of the Proposed Approaches with the Literature  
Comparison of the proposed approaches with the literature presents in Table 6. In order to make the comparison easily, information, including sample size, class count, used classifier and classification accuracy, is given in this table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Sample Size** & **Class Count** & **Classifier** & **Accuracy (\%)** \\ \hline [PERSON], [PERSON], and [PERSON] (2008) [8] & 150 & 3 & ANN & 99.89 \\ [PERSON] et al. (2017) [10] & 850 & 5 & ANN & 99.40 \\ [PERSON], [PERSON], and [PERSON] (2017) [11] & 1000 & 3 & SVM & 99.80 \\ [PERSON] et al. (2019) [12] & 305 & 2 & Deep Auto-encoder Neural & 80.30 \\  & & & Networks & 80.30 \\  & & & GoogleNet & 95.80 \\ [PERSON] et al. (2020) [13] & 958 & 2 & ResNet & 97.20 \\  & & & VGG16 & 95.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed approaches with the literature. Literature studies in the table show that both classical and deep learning-based machine learning classifiers have been used to classify pistachios. In addition, there are hybrid approaches for the classification of pistachios. When the literature studies in the table are examined, there are cases where pistachios are classified as open and closed shelled [8; 15; 19; 22], pistachios are classified as defective or not [12; 13], pistachios are classified as open, closed and rotten [11], spinach mixture in pistachios is detected with CNN [1], and pistachio varieties are classified [10; 14; 16; 17; 18; 21]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Sample Size** & **Class Count** & **Classifier** & **Accuracy (\%)** \\ \hline [PERSON] and [PERSON] (2021) [15] & 3927 & 2 & ResNet50 & 85.28 \\ [PERSON], [PERSON], and [PERSON] (2021) [21] & 2148 & 2 & ResNet152 & 85.19 \\  & & & VGG16 & 83.32 \\ [PERSON], [PERSON], and [PERSON] (2021) [21] & 2148 & 2 & k-NN & 94.18 \\  & & & AlexNet & 94.42 \\ [PERSON] et al.(2022) [17] & 2148 & 2 & VGG16 & 98.84 \\ [PERSON] (2023) [18] & 2148 & 2 & ResNet & 86.16 \\  & & & DNN-based feature & \\ [PERSON] et al. (2023) [14] & 2148 & 2 & extraction and logistic & 97.20 \\  & & & regression & \\  & & & Deep learning-based object & \\ [PERSON], and [PERSON] (2023) [19] & 3700 & 2 & detection for open- and closed-shelled pistachios, respectively & 98 and 85 \\  & & & VGGNet-19 in the LAB color & \\ [PERSON] et al. (2024) [1] & 1200 & 6 & space and applied 5-fold CV, respectively & 100 and 99.16 \\ [PERSON] and [PERSON] (2024) [16] & 6000 & 2 & Augmentation technique and VGG16 & 99.91 \\ [PERSON] et al. (2024) [22] & 2095 & 2 & ResNet18 and clustering & 99.31 \\ \hline  & & & AlexNet (5-fold CV) & 94.88 \\  & & & Proposed MSU-CNN (5-fold & \\  & & & CV) & 96.79 \\  & & & GoogleNet (5-fold CV) & 96.79 \\  & & & VGG16 (5-fold CV) & 97.90 \\  & & & EfficientNet-b0 (5-fold CV) & 98.88 \\  & & & ResNet-18 (5-fold CV) & 99.02 \\  & & & Inception-v3 (5-fold CV) & 99.21 \\  & & & ResNet-50 (5-fold CV) & 99.63 \\  & & & AlexNet (80\% train-20\% test) & 98.84 \\  & & & Proposed MSU-CNN (80\% & \\  & & & train-20\% test) & 97,21 \\ This study & 2148 & 2 & GoogLeNet (80\% train-20\% & \\  & & & test) & 98.37 \\  & & & VGG16 (80\% train-20\% test) & 99.07 \\  & & & EfficientNet-b0 (80\% & \\  & & & train-20\% test) & 99.53 \\  & & & ResNet-18 (80\% train-20\% & \\  & & & test) & 99.53 \\  & & & Inception-v3 (80\% train-20\% & \\  & & & test) & 99.77 \\  & & & ResNet-50 (80\% train-20\% & \\  & & & test) & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._Some of the studies in which pistachio varieties were classified used the same pistachio dataset, while others used different pistachio datasets. From studies, such as [PERSON] et al. (2021) [21], [PERSON] et al. (2022) [17], [PERSON] (2023) [18], and [PERSON] et al.