# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 4 Results and Discussion
For example, in [33], researchers applied GAN-based data augmentation and reported a 29% improvement in mAP for chicken detection models, which aligns with the observations from Table 1 that synthetic data contribute to enhancing model generalization. Similarly, [PERSON] [39] emphasized the importance of synthetic data in preserving essential object characteristics, a principle that was validated by generating FLUX.1 [dev]-based poultry images to supplement a real dataset. The research results also align with research in other domains. For example, in one of the research papers, the authors demonstrated that synthetic data could fully replace real data in tomato disease detection, achieving competitive results with entirely AI-generated datasets [38]. While our study does not suggest replacing real data entirely, the observed model accuracy when training on hybrid datasets confirms that synthetic data have an important role in supplementing real images and reducing manual annotation efforts. Regarding data annotation, using Grounding DINO and SAM2 for this research follows similar efforts by Roboflow [50], which has reported significant reductions in annotation time by leveraging automated tools. These efficiency gains align with industry trends emphasizing automation in dataset preparation to streamline deep learning workflows. The finding in this research confirms that automated annotation is particularly beneficial in agricultural applications, where dataset labeling is one of the major bottlenecks. Comparing our best-performing model (300 real, 100 synthetic images, mAP = 0.829) with other poultry detection studies, our results are competitive but as already mentioned, this was not the focus of this study. Experiments from this research demonstrate that a hybrid dataset can maintain high accuracy while reducing dependency on extensive manual image collection. These findings contribute to the broader discussion on generative AI applications in precision agriculture. The results reinforce that synthetic data can effectively supplement real-world datasets and improve model accuracy in poultry monitoring. This research is part of the agroNET platform [42] and contributes to a broader in-production platform ecosystem. In this study, ethical considerations were taken into account regarding data privacy and the use of synthetic data. The synthetic images generated using FLUX.1 [dev] are entirely new and do not replicate or reconstruct real individuals, ensuring compliance with data protection regulations. By using synthetic data, the need for real image collection from farms is reduced, minimizing potential privacy concerns related to real-world data. Additionally, the dataset used in this research is publicly available via Roboflow, ensuring transparency and reproducibility while adhering to ethical AI principles. Some limitations of this study include its focus on a single farm with specific environmental conditions. Although the fine-tuning dataset included diversity in chicken sizes, additional variations, such as different surfaces, chicken colors and environmental settings, could further improve the model's robustness. The dataset could also be expanded with more samples and tested on latest state-of-the-art computer vision models, such as YOLOv11 and YOLOv12. Additionally, further testing and parameter optimization could enhance the generation of more realistic images. To support a deeper statistical analysis, techniques like cross-validation could be implemented on a larger validation dataset.