# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 4 Results and Discussion  
The results presented in Table 1 provide insights into the impact of synthetic data on the YOLOv9-e model for object detection. YOLOv9-e was chosen as a strong representative of modern YOLO architectures to provide a consistent baseline for evaluating synthetic data augmentation rather than benchmarking different YOLO versions. The initial experiment (Experiment 1) evaluated the baseline model without additional training data. The results show a relatively low mAP score of 0.245, which aligns with previous findings. These results confirm that, without additional annotated data or fine-tuning, the model struggles to achieve high accuracy. A clear trend is observed when comparing Experiments 2 and 3. Increasing the amount of real data leads to a notable improvement in model accuracy, rising from 0.796 to 0.822. This finding supports the well-established principle that expanding real datasets enhances model performance. However, Experiments 4 and 5, which assess the effect of using only synthetic data, reveal that increasing synthetic data alone does not improve accuracy (0.793 vs. 0.789). An important observation emerges when comparing Experiments 6 and 7. A combination of real and synthetic data leads to an mAP score of 0.821, closely matching the performance of the model trained exclusively on real data (0.822). This suggests that integrating synthetic and real data helps maintain high accuracy while reducing the need for extensive real-world data collection. Furthermore, Experiment 8 achieves the highest mAP score (0.829). These results indicate that adding a small percentage of synthetic data to real data can slightly improve model accuracy, which aligns with similar studies [40]. This improvement is likely due to better generalization introduced by synthetic data. In experiments 10-15, data augmentation was applied in three different ways: using only real images, only synthetic images, and a combination of both. The results indicate that augmentation did not improve model accuracy, despite increasing dataset size and diversity. The applied augmentation techniques included rotation, brightness adjustment (\(-\)15% to +15%), saturation change (\(-\)10% to +10%), and cropping (max 10%). These findings indicate that augmentation alone is not sufficient for improving performance when working with synthetic datasets. This can be attributed to the differences between the test data and augmented data, as well as the impact of multiple transformations. For instance, in Experiment 15, where data augmentation was applied only through rotation, the mAP was slightly lower compared to Experiment 8, which yielded the best results, but better compared to other experiments with mixed data augmentation. As mentioned, the purpose of this study was not to compare results with previous mAP values but rather to highlight the potential role of synthetic data in developing predictive models. Even though comparing mAP values with other research papers was not the primary focus of this study, the results obtained with a smaller dataset and a limited number of epochs remain comparable to existing studies in this field [4]. Based on the results and existing research, synthetic data can significantly enhance machine learning model training for chicken detection, particularly when real data are limited or unavailable. As generative AI models for synthetic data generation continue to evolve, their effectiveness in model training and dataset augmentation is expected to improve. However, it is essential to ensure that synthetic data accurately represent real-world characteristics to achieve high model precision. This research demonstrates that synthetic datasets and generative AI can significantly reduce the need for real data collection while maintaining high accuracy in YOLO-based object detection models. Studies such as [35; 36] have demonstrated that generative AI significantly improves object detection accuracy in agricultural applications by addressing dataset limitations.