# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
###### Abstract  
This research explores the role of synthetic data in enhancing the accuracy of deep learning models for automated poultry farm management. A hybrid dataset was created by combining real images of chickens with 400 FLUX.1 [dev] generated synthetic images, aiming to reduce reliance on extensive manual data collection. The YOLOv9 model was trained on various dataset compositions to assess the impact of synthetic data on detection performance. Additionally, automated annotation techniques utilizing Grounding DINO and SAM2 streamlined dataset labeling, significantly reducing manual effort. Experimental results demonstrate that models trained on a balanced combination of real and synthetic images performed comparably to those trained on larger, augmented datasets, confirming the effectiveness of synthetic data in improving model generalization. The best-performing model trained on 300 real and 100 synthetic images achieved mAP = 0.829, while models trained on 100 real and 300 synthetic images reached mAP = 0.820, highlighting the potential of generative AI to bridge data scarcity gaps in precision poultry farming. This study demonstrates that synthetic data can enhance AI-driven poultry monitoring and reduce the importance of collecting real data. computer vision; deep learning; digital farm management; generative artificial intelligence; smart farms; synthetic data +
Footnote †: journal:such as temperature, humidity, CO\({}_{2}\), and ammonia levels, which are vital for poultry health. The global demand for food is projected to rise by 60% between 2010 and 2050, with poultry production playing a crucial role in meeting this need [3]. By automating monitoring and analysis, AI can significantly reduce labor demands, improve feed efficiency and lower mortality rates. A previously conducted study investigated the application of deep learning for real-time monitoring in poultry farms using edge AI devices equipped with cameras [4]. AI-based models were developed for chicken detection and segmentation, enabling automated functions such as counting, mortality detection, and growth assessment. The research utilized Faster R-CNN (Regional Convolutional Neural Network) architectures, with AutoML employed to identify the most suitable model for the given dataset. The optimized models achieved high accuracy (mAP = 85% for object detection and mAP = 90% for instance segmentation). These models were deployed on edge AI devices and evaluated in real poultry farm environments, demonstrating their potential for early disease detection and automated farm management. The findings highlighted the effectiveness of integrating computer vision (CV) and the Internet of Things (IoT) in poultry farming, though further dataset expansion and model refinement were identified as necessary for improved performance. Recent advancements in object detection have significantly improved the YOLO (You Only Look Once [5]) family of models, compared to earlier frameworks like Detectron2 with models such as Faster R-CNN [6] and Mask R-CNN [7]. The YOLO architecture enables real-time processing with high precision, especially with new versions [8]. YOLO-based models offer superior inference speed and real-time processing capabilities, making them highly suitable for applications requiring low-latency detection [9; 10; 11]. Recent evaluations demonstrate that YOLOv8 outperforms two-stage detectors such as Faster R-CNN and Mask R-CNN in agricultural and industrial environments, achieving higher mAP scores with reduced computational cost [10; 11]. Additionally, studies show that optimized YOLO models can maintain competitive accuracy while operating at significantly higher FPS (frames per second) compared to traditional architectures [9], further reinforcing their advantage in real-time object detection scenarios. Today, various YOLO models are available, each offering slightly different performance characteristics (Figure 1). The most recent version is YOLOv12 [12].