## 4 Framework
### HPA-C Framework
State-of-the-art architectures, such as Vision Transformers (ViTs) and large-scale convolutional networks, require substantial memory and processing power, making their deployment infeasible on constrained hardware. This limitation restricted the scope of model exploration and prevented the utilization of potentially more powerful architectures. Future work can address this challenge by leveraging model compression techniques such as quantization, pruning, and knowledge distillation to optimize memory efficiency. Additionally, distributed training using frameworks like TensorFlow and PyTorch 2.0 across multiple GPUs or cloud-based solutions, such as Google Colab, AWS, or TPUs, could enable the training of high-capacity models without hardware bottlenecks. The datasets used in this study vary in terms of image quality, resolution, and class distribution, reflecting real-world challenges in smart agricultural applications. All datasets contain RGB images, but their resolutions differ. This difference could potentially introduce inconsistencies in feature representation. The preprocessing pipeline, including image resizing and normalization, was designed to mitigate such variations and ensure a consistent input format for the CNN. Our study does not explicitly focus on image quality variations such as noise, blur, or lighting conditions, which could influence model performance. Future work could explore image enhancement techniques to improve robustness against such factors. Class imbalance is a problem in datasets where certain diseases or conditions are under-represented and could impact classification performance. This issue is especially concerning in minority classes. Although the proposed model demonstrated strong performance across diverse datasets, class-balancing techniques such as oversampling (e.g., SMOTE and ADASYN) or cost-sensitive learning could further enhance generalization. We could use use advanced synthetic data generation approaches, including Generative Adversarial Networks (GANs) and diffusion models. These could also help address imbalance-related challenges by generating realistic samples for under-represented categories. Despite these dataset limitations, the model consistently outperformed benchmark methods. This demonstrates its robustness in handling diverse agricultural imagery. Future research should systematically analyze the impact of resolution variations, noise levels, and class distribution on classification accuracy to provide deeper insights into how dataset quality affects feature selection and CNN performance. The prospects of using Python-based tools (v3.13.2) can also be explored. Tools like PyTorch's weighted loss functions for cost-sensitive learning and imbalanced learning for oversampling can be explored. To ensure that these balancing procedures are effective in a variety of settings, future research should assess how they affect classification performance. The primary focus of this research was tilted towards smart agricultural datasets. There is still room to expand the concept to other fields, such as medical imaging. However, direct application of medical imaging datasets is difficult because there is extreme class imbalance. These datasets also require specialized preprocessing. Future studies could investigate the creation of sophisticated preprocessing pipelines that fulfill image processing needs by utilizing Python libraries like OpenCV and MONAI. This study compares HPA-C with well-known optimization-based approaches in order to assess its efficacy as a feature selection strategy. The chosen benchmarks guarantee an equitable evaluation of HPA-C's contributions. We illustrate that the observed advantages are due to our hybrid strategy and not due to incidental factors in comparison with separate heuristic techniques. We see the potential for more insights from comparisons with end-to-end deep learning models and hybrid metaheuristic techniques.