# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 2 Related Work  
Modern poultry farming increasingly incorporates deep learning techniques to automate different tasks such as bird detection, animal counting, behavior analysis, mortality detection, etc. Deep learning models have improved automatic poultry counting, addressing challenges like occlusions and high-density environments, making farm management more efficient [21; 22; 23; 24; 25]. Behavior and movement analysis enables the early detection of anomalies, helping to monitor poultry welfare and identify potential health issues or stress factors [26; 27; 28]. These methods contribute to optimizing farm operations and improving animal well-being. Furthermore, deep learning is used for detecting deceased animals, which is crucial for disease prevention and biosecurity measures. AI-driven detection systems help automate this process, ensuring timely intervention and reducing manual labor requirements [29]. By integrating these solutions, poultry farming can enhance efficiency, reduce costs, and improve overall livestock management. Object detectors like YOLO and R-CNN variants have been widely applied in broiler and layer house environments [30; 31]. YOLO-based models in particular have shown high accuracy in detecting chickens and even classifying their health status. For example, a thermal YOLOv8 model achieved a mAP of 98.8% in identifying broilers and pathological signs under challenging lighting [30]. The combination of YOLOv8 with DeepSORT tracking to monitor cage-free hens reached 94% multi-object tracking accuracy and detecting abnormal behaviors like smothering and piling [32]. These one-stage detectors offer a notable speed advantage: an augmented YOLO model ran at 37 FPS with 0.84 mAP, a 29% accuracy improvement over prior networks, far outpacing two-stage approaches [33]. Traditional two-stage detectors (e.g., Faster R-CNN) have also been used for poultry. Improved Faster R-CNN was used to detect stunned broilers, attaining 98% accuracy in poultry processing lines [34]. However, such R-CNN models are computationally heavier, often achieving only a few FPSs, whereas YOLO variants enable near real-time monitoring. For finer analysis, Mask R-CNN has been explored for segmenting birds and behaviors. Another example is with Mask R-CNN used to recognize the preening behavior of hens, reporting 94% accuracy and robust recall/specificity in identifying individual hens' actions [31]. This highlights that both one-stage (YOLO) and two-stage (Faster/Mask R-CNN) frameworks can successfully detect poultry and their activities, though YOLO-based methods are generally favored for real-time farm deployment due to their speed and adequate precision. One of the challenges in applying deep learning to poultry monitoring is data scarcity--collecting and labeling farm images is labor intensive and constrained by seasonality and environmental variability [35]. To address this, researchers have turned to generative AI for augmenting agricultural datasets. GAN-based data augmentation has become a focal approach in agri-vision research, yielding more diverse and balanced training images [35]. Using a GAN-MAE pipeline to generate synthetic chicken face images demonstrated improvement in detection accuracy by over 29% (mAP 0.84) compared to models without augmentation [33]. Similarly, a systematic review of GANs in agriculture highlighted substantial improvements in image classification and detection tasks with the addition of synthetic images [36]. Beyond GANs, diffusion models are emerging as powerful data generators. Recent work in the plant domain shows diffusion models producing highly realistic disease images, outperforming GANs in fidelity [37]. This suggests a strong potential to apply diffusion-based augmentation in poultry vision, although it remains largely untapped in animal agriculture to date [35]. Innovative strategies have also been proposed to generate entire synthetic datasets.