## 4 Experimental Results
The loss decreased gradually, indicating efficient model convergence without obvious signs of overfitting. DeiT performed similarly to MaxViT, with accuracy steadily increasing toward 98%. The loss gradually decreased, confirming the stability of the model during training. Validation performance was slightly more variable, but still very good, showing robust generalization ability. XCIT showed excellent performance, with accuracy steadily increasing to 99%. The loss decreased gradually, suggesting efficient learning of relevant features from the data. The model demonstrated excellent generalizability with consistent performance across training and validation sets. ViTBase achieved good performance, with accuracy steadily increasing toward 99%. The loss gradually decreased, indicating efficient convergence and robust generalization ability. The model showed excellent stability during training. FasterViT performed well, with accuracy steadily increasing to 99.1%. The loss decreased gradually, indicating efficient convergence and great generalization ability. The model demonstrated stable performance in training and validation, making it suitable for deployments on resource-constrained devices. As per the previously attached results, the evolutions of the loss and accuracy functions for the IP102 dataset and the table of performance indicator values are presented. For the IP102 dataset, for each proposed architecture, the obtained results are shown in Table 2, and the performance evolution is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline ViT Base & 0.985 & 0.986 & 0.987 & 0.985 \\ DEiT & 0.976 & 0.976 & 0.977 & 0.975 \\ MaxViT & 0.981 & 0.981 & 0.982 & 0.980 \\ FasterViT & 0.990 & 0.990 & 0.991 & 0.989 \\ XCIT & 0.987 & 0.987 & 0.980 & 0.986 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: D0 dataset results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline ViT Base & 0.7953 & 0.7940 & 0.7971 & 0.7783 \\ DEiT & 0.8100 & 0.7996 & 0.8059 & 0.8074 \\ MaxViT & 0.8092 & 0.8088 & 0.7977 & 0.7943 \\ FasterViT & 0.8156 & 0.8145 & 0.8341 & 0.8086 \\ XCIT & 0.8119 & 0.8108 & 0.8290 & 0.8169 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IP102 dataset results. As with the D0 dataset, the ViT architecture also showed notable performance on the IP102 dataset, underscoring the power of these architectures to extract features relevant to the classification process, given a larger and multi-class insect pest dataset. By comparing the models, the FasterViT architecture demonstrated the best performance concerning the insect pest dataset, with an \(F1\) score of 0.8145. These can be seen in the balanced metrics and stable curves on the training and validation sides. A model like this architecture is  
Figure 3: Accuracy during training and validation over 100 epochs for D0 dataset models: (**a**) DeiT, (**b**) FasterViT, (**c**) MaxViT, (**d**) ViT Base, and (**e**) XCiT. Training curves indicate stable convergence behavior with minimal overfitting. XCIT, which had good values for accuracy (0.8119), recall (0.829), and \(F1\) score (0.8018), but slightly higher validation losses compared to FasterViT. MaxViT, DeiT, and ViTBase had performance but showed slight instabilities and fluctuations on the training and validation side compared to the previously mentioned top models. Figure 4: Accuracy during training and validation over 100 epochs for IP102 dataset models: (**a**) DeiT, (**b**) FasterViT, (**c**) MaxViT, (**d**) ViT Base, and (**e**) XCiT. Training curves indicate stable convergence behavior with minimal overfitting. For the testing part, the characteristics of the proposed method were considered. The final operational scheme for classifying an image started with sending an image to each base model and preprocessing it to match the base model's input.