## 5 Discussion
Future work could explore adaptive model selection or context-aware ensembles to further improve scalability and robustness. Another limitation is the lack of repeated runs or k-fold cross-validation. While a large and diverse test set was used for each  
In future work, we aim to expand the simulation framework by incorporating additional fusion strategies, such as soft-voting ensembles, dynamic expert selection, and gating-based adaptive ensembling. These approaches may further improve robustness in variable agricultural conditions. Additionally, while the current study used accuracy, precision, recall, and F1 score as evaluation metrics--aligned with existing literature--future evaluations may include more nuanced metrics, such as ROC-AUC, to provide deeper insight into classification performance across unbalanced classes. The current study employed logistic regression as the meta-classifier due to its interpretability, low computational cost, and strong performance when working with well-structured inputs, such as weighted logits. Its simplicity helped reduce the risk of overfitting, particularly given the relatively low dimensionality of the meta-feature space. However, we acknowledge that more complex classifiers, such as multilayer perceptron, random forests, or gradient-boosted trees, may capture higher-order interactions between model predictions. Exploring these options represents a valuable future direction to potentially improve ensemble performance, especially under more challenging data distributions. It is important to acknowledge potential sources of bias in the present methodology. The architectural selection was based on model diversity and training feasibility but did not include certain high-performing alternatives, such as Swin Transformer or BEiT, which could influence comparative outcomes. Dataset bias is also present, as both D0 and IP102 are geographically constrained and may not fully represent pest species or environmental conditions from other regions. To mitigate overfitting, several regularization strategies were employed, including dropout (rate = 0.5), L2 weight regularization, early stopping, and basic data augmentation (e.g., flips and brightness adjustment). While these methods helped stabilize training, the fixed training/validation/test split limited the scope for performance generalization. Furthermore, no domain adaptation techniques were applied to test transferability. In future work, we plan to incorporate datasets from diverse agroclimatic zones and evaluate the ensemble system's robustness in cross-region pest classification scenarios. This will help validate its generalizability and identify region-specific adaptation needs. Another practical consideration is the deployment of the ensemble model in hardware-constrained environments, such as edge computing devices used for real-time pest monitoring in agricultural fields. Although architectures like FasterViT are inherently optimized for speed, the overall ensemble system may still be computationally intensive. To address this, several model compression and acceleration techniques can be applied:  
1. Model pruning to remove redundant parameters without significant accuracy loss. 2. Quantization to reduce weight precision (e.g., from 32-bit float to 8-bit integer) and improve inference speed. 3. Knowledge distillation to train a smaller model that mimics the ensemble's output. 4. Lightweight ViT variants, such as MobileViT, TinyViT, or EdgeFormer, for resource-efficient inference. 5. Inference optimization frameworks, like ONNX Runtime, TensorRT, or TFLite, for deployment on embedded devices. Integrating these strategies would help bridge the gap between high-performing ViT ensembles and real-time pest detection systems suitable for use in farms, drones, or mobile platforms. Future work could explore these directions for sustainable deployment.