## 5 Discussion and Conclusions
In addition to the significant contributions of AYOLO, future work will focus on optimizing key components such as the Feature Alignment Module (FAM) and Information Fusion Module (IFM) to further enhance the model's efficiency and applicability. We specifically aim to target the following:  
Adaptive weighting mechanisms: Dynamically assigning weights to feature maps to prioritize task-relevant information. Integration of advanced attention mechanisms: Combining FAM with lightweight channel and spatial attention modules to improve contextual feature extraction. Hardware-specific optimizations: Tailoring computational operations to GPUs, TPUs, or edge devices to maximize performance for real-time applications. Quantification of RepConv's impact: Evaluating the trade-offs between computational savings and accuracy across diverse hardware platforms to further validate its effectiveness in the IFM. Simplification for edge devices: Exploring streamlined versions of the FAM and RepConv for deployment in resource-constrained environments such as UAVs and IoT devices. These directions aim to expand AYOLO's capability to handle diverse datasets, improve deployment scalability, and achieve enhanced real-time performance under varying operational constraints. By addressing these areas, AYOLO can continue to serve as a robust and adaptable framework for real-time object detection. The advancements in YOLO-based architectures underscore the importance of domain-specific modifications for enhancing performance. For instance, integrating preprocessing algorithms, as seen in the dehazing-enhanced YOLO-v10 for ship fire detection, demonstrates how preprocessing can address environmental constraints. Similarly, the feature aggregation mechanisms in Gold-YOLO and the lightweight architecture of YOLO-WDNet highlight strategies for improving computational efficiency while maintaining accuracy. The feature enrichment methods employed for drone-based weed detection further emphasize the value of refined feature representation in addressing small object detection challenges. Inspired by these works, AYOLO incorporates domain-specific optimizations, such as attention mechanisms and feature alignment, to enhance its capability for detecting clandestinely cultivated plants in UAV imagery. Future iterations of AYOLO could explore integrating preprocessing algorithms or lightweight architectures to further enhance performance in resource-constrained environments. These directions align closely with the approaches highlighted in recent research, reinforcing the importance of continuous innovation and application-specific tuning. In conclusion, AYOLO represents a promising solution for object detection tasks involving small datasets, providing accurate and reliable detection with relatively low computational overhead. Its performance in detecting poppy plants from a limited dataset highlights its potential applications in specialized agricultural or environmental monitoring tasks where dataset sizes are limited. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15052718/s1](https://www.mdpi.com/article/10.3390/app15052718/s1)]([https://www.mdpi.com/article/10.3390/app15052718/s1](https://www.mdpi.com/article/10.3390/app15052718/s1)). Methodology, A.Y. and Y.Y. ; Software, A.Y. ; Validation, A.Y. ; Formal analysis, A.Y. and Y.Y. ; Resources, A.Y. ; Data curation, Y.Y. ; Writing--original draft, A.Y. ; Writing--review &editing, A.Y. and N.Y. N.Y.; Supervision, A.Y. and N.Y. All authors have read and agreed to the published version of the manuscript. * This research received no external funding. * [PERSON] _et al._ (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Faster R-CNN: Towards real-time object detection with region proposal networks. _IEEE Trans. Pattern Anal. Mach. Intell._**2016**, _39_, 1137-1149. [CrossRef] [PubMed]
* [PERSON] _et al._ (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Mask r-CNN.