## 4 Experimental Results  
The main steps that define the structure and the approaches for the experimental results are represented by the analysis and the experiments performed on the individual models, choosing the right architectures for the decision fusion system, and experimental results with the decision fusion system, the meta-classifier approach. The transfer learning technique involved pretrained models on a vast dataset (such as ImageNet) and their adaptation to a new specific task, insect pest classification. Using PyTorch, the models were implemented with the TIMM library ([[https://huggingface.co/docs/timm/index](https://huggingface.co/docs/timm/index)]([https://huggingface.co/docs/timm/index](https://huggingface.co/docs/timm/index)); accessed on 10 May 2025). The pretrained ViT models were used, and fine-tuning techniques were applied to adjust the model parameters on the chosen datasets--D0 and IP102. The fine-tuning part involved additional training of chosen models on a new dataset. In the same framework, a lower learning rate was adopted to avoid the problem of overlearning, but also to use the prior knowledge of the model. The initial step was to train and evaluate the base models. This step aimed to score the performance of individual models and perform rigorous training and validation sessions. For the present study, conducting these training and evaluation sessions had several essential roles: the objective evaluation of the basic models, the optimization of parameters and performances, and the prevention of unwanted cases of overfitting/underfitting were considered. Therefore, this step represents strong points for improving the robustness and effective development of the ensemble decision system. Models trained on the defined 100 epochs featured a regular performance check to prevent overlearning. For fine-tuning all ViT-based models, the following hyperparameters were used consistently: The batch size was set to 32, and the Adam optimizer was employed with an initial learning rate of 0.001. A cosine annealing scheduler was used to gradually reduce the learning rate over the 100 training epochs. A dropout rate of 0.5 was applied to the fully connected layers of each model to mitigate overfitting. L2 regularization (weight decay) was set to \(1\times 10^{-4}\), applied through the optimizer. These settings were empirically chosen based on stability across datasets and alignment with best practices in ViT-based transfer learning. To evaluate the practical viability of our ensemble architecture, we recorded both the training and inference times for each model. Individual ViT models required, on average, between 3.4 and 4.8 h for full training (100 epochs) on the D0 dataset and up to 6.5 h on IP102. Inference time per image (averaged across all classes) was approximately ViT Base: 15.2 ms, DeiT: 13.8 ms, MaxViT: 21.1 ms, FasterViT: 11.9 ms, and XCiT: 16.6 ms. The meta-classifier training phase, based on the logit-weighted features, required under 5 min, and its inference time per image was negligible. The overall ensemble inference time, including all base models and the meta-classifier, was approximately 78 ms per image, demonstrating feasibility for near-real-time deployment in field monitoring systems. This tuning strategy helped balance training efficiency with model generalization and stability across datasets. These techniques reduced the risk of overlearning and increased the robustness of the model concerning the datasets used. They were effective when working with many features. For the D0 dataset, for each proposed architecture, the results obtained are shown in Table 1.  
The evolution of the accuracy function after training and validating the chosen models is presented in Figure 3.  
For each graph, the horizontal axis represents the number of training epochs. The vertical axis shows the accuracy values. For the D0 dataset, MaxViT demonstrated a gradual and steady increase in accuracy in training and validation, reaching values close to 99%.