## 5 Discussion  
ViT architecture using transfer learning with fine-tuning provided significant insights into the performance and applicability for insect pest classification. Several challenges and limitations in the methodologies used are noted. First, using singular models for pest detection can reveal limitations that bring to the fore the need for massive optimization of architecture and the use of dedicated hardware with enormous resources to maximize performance. This comes at the cost of performance, as such systems cannot be easily integrated to create continuous monitoring systems, as computing and processing require significant hardware resources. However, there is research on such architecture development and optimization for low-resource mobile systems. By optimization techniques, such as lower learning rates, dropout, and regularization strategies, it was observed that the risks of overfitting can be avoided while maintaining the benefits of the pretrained weights of the chosen models. This approach allowed the ViT models to be efficiently adapted to new tasks, achieving high performance and good generalization across the datasets. The distribution of insects from the D0 dataset provided relevant training and evaluation data that resulted in maximum values for the proposed architecture in this study, with the ensemble model and individual models achieving good metrics and high generalization ability. In the case of the IP102 dataset, the insect classes represented were in greater numbers, but their distribution was not equal for each class, so there was no significant balance. It was observed that the proposed models tended to misclassify images due to the limitations present in the dataset. It can be noted that the analyzed IP102 dataset needs to present careful preprocessing and balancing modules of the presented instances to provide \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline D0 & 0.9987 & 0.9982 & 0.9980 & 0.9975 \\ IP102 & 0.8371 & 0.8425 & 0.8520 & 0.8200 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ensemble system test results for the D0 and IP102 datasets. improved performance. By combining multiple ViT models and using a meta-classifier, the ensemble architecture can reduce overfitting, achieving better generalizations on new datasets, a powerful technique to refine the predictions of multiple individual ViT models to maximize the accuracy and robustness of an ensemble model. The evaluation of individual models, including VitBase, DeiT, MaxViT, FasterViT, and XCIT, provided a detailed understanding of the strengths and limitations of each architecture. For the D0 dataset, all models achieved high performance, with FasterViT achieving the highest performance values. On the second dataset, IP102, which was more challenging due to its complexity and multi-class structure, FasterViT also achieved good performance, closely followed by XCiT. These details highlighted the ability of the ViT models in pest identification under various conditions and scenarios. The decision fusion system improved the classification performance by combining the results of multiple base models using a meta-classifier. This ensemble system leveraged the complementary strengths of the chosen individual models, assigning weights based on their performance, as described by \(F1\) scores. The results demonstrated that the ensemble outperformed the individual models, highlighting the effectiveness of decision fusion in addressing the limitations of independent architecture. In analyzing the training dynamics, moderate fluctuations were observed in the validation performance of certain models, notably DeiT and ViT Base, especially when trained on the more complex and imbalanced IP102 dataset. These fluctuations may stem from the smaller representational depth of these models compared to newer ViT variants, as well as their sensitivity to minority classes during learning.