# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Limitations  
Dataset Size and Diversity: Although the dataset comprising 2462 animal images represented a significant volume, the diversity within this dataset, particularly regarding environmental settings and animal behaviors, may not have been adequate to fully evaluate the generalizability of the deep learning models. More extensive datasets that encompass a wider range of scenarios and animal interactions could provide a more robust assessment of model performance.  
Model Generalization: The models were validated using a specific dataset, which, despite its variety, represented a controlled environment. Consequently, the efficacy of these models in real-world scenarios--where factors such as lighting, weather, and landscape vary significantly--might not be as robust as indicated under controlled study conditions.  
Hardware Limitations: The models' performance was optimized for specific hardware setups that might not be universally available in practical, real-world farm settings. Dependence on high-performance GPUs for model training and inference poses significant limitations for deployment in resource-constrained environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Algorithm Base** & **Key Parameters** & **Configuration Details** \\ \hline YOLOv8 & YOLO & Batch size: 16, Epochs: 100, Decay: 0.9, Weight\_Decay: 0.0001, score\_threshold = 0.01, nms\_top\_k = 1000, max\_predictions = 300, nms\_threshold = 0.7, reg\_max = 16, Optimizer: [PERSON] & Resolution: 640 \(\times\) 640. Trained on extensive datasets with multi-scale prediction capabilities. Anchor-free framework. \\ \hline Yolo-NAS & NAS with YOLO & nms\_top\_k = 1000, max\_predictions = 300, nms\_threshold = 0.7, reg\_max = 16, Optimizer: [PERSON] & Real-time object detection optimized for speed and accuracy. Uses quantization-aware blocks. \\ \hline Fast-RNN & ResNet-based RNN &
\begin{tabular}{c} Utilizes residual connections for stability. Less computationally intensive compared to traditional RNNs. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the applied models in this study.  
Balancing Precision and Recall: While the models demonstrated commendable precision and recall rates, the balancing of these metrics is critical for practical applications. For example, the Yolo-NAS model exhibited high recall but lower precision, potentially leading to a high rate of false positives in actual deployments, which could compromise the system's usability.  
Impact of Non-Visual Factors: The current study focused exclusively on visual data for threat detection. However, in actual agricultural settings, other modalities such as acoustic signals, thermal imagery, and olfactory data could significantly enhance threat detection capabilities. Integrating these additional data types could improve detection rates and diminish the incidence of false positives.  
Real-Time Processing Needs: Although the speed of the models was addressed, the real-time processing capabilities necessary in live environments--where decisions must be made swiftly to avert animal attacks--were not thoroughly tested.  
Recommendations for Future Research: Addressing these limitations in future studies by broadening dataset diversity, testing models in more varied and realistic environments, integrating multimodal data, and evaluating deployment feasibility in typical farm settings with varying technological access could significantly enhance the practical applicability of the findings. Such improvements would pave a clearer pathway for deploying these technologies to bolster farm animal safety and enhance farm security.