# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 5 Conclusions  
This study investigated the potential of synthetic data in enhancing object detection models for AI-driven poultry farming. The findings demonstrate that synthetic images generated with FLUX.1 [dev] can effectively supplement real-world data, particularly in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment ID**} & \multicolumn{4}{c}{**Dataset Split**} & \multirow{2}{*}{**Metric**} \\ \cline{2-2} \cline{4-6}  & **Real** & **Synthetic** & **Augmented** & **Total** & **mAP** \\ \hline
1 & 0 & 0 & 0 & 0 & 0.245 \\
2 & 100 & 0 & 0 & 100 & 0.796 \\
3 & 400 & 0 & 0 & 400 & 0.822 \\
4 & 0 & 100 & 0 & 100 & 0.793 \\
5 & 0 & 400 & 0 & 400 & 0.789 \\
6 & 50 & 50 & 0 & 100 & 0.801 \\
7 & 200 & 200 & 0 & 400 & 0.821 \\
8 & 300 & 100 & 0 & 400 & 0.829 \\
9 & 100 & 300 & 0 & 400 & 0.820 \\
10 & 400 & 0 & 800 & 1200 & 0.820 \\
11 & 0 & 400 & 800 & 1200 & 0.792 \\
12 & 200 & 200 & 800 & 1200 & 0.814 \\
13 & 300 & 100 & 800 & 1200 & 0.812 \\
14 & 100 & 300 & 800 & 1200 & 0.808 \\
15 & 400 & 0 & 800 & 1200 & 0.827 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The impact of synthetic data on the accuracy of the YOLOv9-e model, tested on 50 real images, object detection. Number of training epochs: 100, batch size: 16.  
cases where data collection is challenging or resource-intensive. Through experimental validation, it was observed that models trained on a combination of real and synthetic images achieved comparable accuracy to those trained exclusively on real data. This highlights the potential of synthetic data to improve model generalization, reduce data dependency, and enhance robustness in practical applications. A key contribution of this research is the development of a hybrid dataset that integrates real and synthetic images, offering a scalable and cost-effective approach to dataset expansion. The implementation of an automated annotation pipeline using Grounding DINO and SAM2 significantly reduced the manual effort required for labeling, streamlining the dataset preparation process. The results further confirm that while synthetic data alone do not outperform real data, their strategic integration into training datasets improves model efficiency and reduces the reliance on large-scale real-world data collection. This finding is particularly valuable for precision agriculture, where the availability of high-quality labeled datasets remains a major challenge. Despite these advancements, there are still areas for further exploration. Future research should focus on expanding dataset diversity, including synthetic large-scale image and video datasets, to improve model generalization and enhance the realism of synthetic data by incorporating samples from various farm environments. Additionally, fine-tuning the balance between real and synthetic data under varying environmental conditions and object complexities could provide deeper insights into optimal dataset compositions. Beyond poultry farming, the methodologies introduced in this study could be adapted to other domains of smart agriculture, where synthetic data generation and automated annotation improve AI model development. Leveraging High-Performance Computing (HPC) will be essential for efficiently processing large datasets, enabling faster training, real-time validation, and broader applications in dynamic monitoring and precision agriculture. By continuously refining synthetic data generation and integration techniques, future advancements can lead to more scalable, accessible, and cost-effective AI solutions across agriculture and related fields. Conceptualization, T.P. and S.K. ; Methodology, S.C. and T.P. ; Software, S.C., I.J. and D.B. ; Supervision, T.P. and S.K. ; Validation, S.C., I.J. and D.B. ; Writing--original draft, S.C. and T.P. All authors have read and agreed to the published version of the manuscript. This research is supported in part by projects HPC4S3 ME and EuroCC2/EuroCC4 SEE. HPC4S3 ME received funding through IPA II program, CFCU/MNE/213, call reference EuropeAid/172-351/ID/ACT/ME.