## 4 Discussion  
The CCS-YOLOv8 model is primarily utilized for the recognition of common livestock in the Qinghai region and holds potential applications in other animal and remote sensing target detection tasks. Despite significant improvements over the baseline model, we acknowledge that there are still instances of false alarms and missed detections. To address these issues, we plan to further optimize the model's training process by adjusting hyperparameters and exploring ensemble methods, such as combining multiple models to leverage their strengths and mitigate individual weaknesses. These steps are expected to enhance the overall detection accuracy and reliability of the model.  
Our current dataset primarily includes three types of grasslands: alpine meadows, alpine steppes, and alpine desert grasslands. To improve our model's adaptability to diverse environments, we plan to collect additional datasets with varied backgrounds in the future. This will help ensure that our dataset encompasses all types of natural grasslands and improve the model's performance in various environmental contexts.  
To further broaden the utility and applicability of our model, we intend to expand its capabilities to detect additional livestock species in future work. This expansion will involve including other commonly raised livestock species, thereby enhancing the model's performance and relevance in regions with diverse livestock populations.  
As the model aims to strike a balance between accuracy and real-time processing capability, exploring lightweight optimization techniques is crucial. Techniques such as network pruning, quantization, and knowledge distillation can reduce model complexity without sacrificing performance, enabling more efficient inference on resource-constrained devices.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **F1-Score (\%)** \\ \hline YOLOv8n & 40.4 & 29.7 & 27.3 & 14.9 & 15.0 & 34.2 \\ CCS-YOLOv8 & 42.6 & 33.5 & 31.1 & 16.8 & 17.1 & 37.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the robustness test.  
Going forward, the CCS-YOLOv8 model will be integrated into monitoring platforms such as drones and intelligent robots to refine the intelligent detection process and achieve real-time video detection of common livestock. This integration aims to enable proactive management strategies such as automated alerts for overgrazing, thereby enhancing the practical utility and impact of the model in real-world applications. By leveraging these advanced monitoring platforms, the model can provide timely and accurate livestock management support, ensuring sustainable and efficient livestock farming practices.  
To ensure the continued effectiveness and relevance of the CCS-YOLOv8 model in evolving livestock management contexts, we plan to establish a framework for continuous evaluation and feedback. This framework will involve collecting feedback from end-users, monitoring the model's performance in different scenarios, and incorporating new data and insights into the model refinement processes. By implementing this continuous evaluation and feedback loop, we aim to iteratively improve the model over time, ensuring its adaptability and utility in various real-world applications. This approach will help us address emerging challenges and maintain the model's high performance in diverse livestock management contexts.