# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
###### Abstract
While newer YOLO versions often introduce architectural improvements, they do not necessarily guarantee better accuracy or efficiency across all applications [13]. Several factors influence the performance of different YOLO versions, including dataset characteristics, object complexity, model size and hardware compatibility. In some cases, earlier YOLO versions may outperform newer ones due to their lighter architectures and optimized processing speeds for specific tasks. Since the primary objective of this study was to analyze how synthetic data influence model accuracy, the selection of the latest YOLO model was not a critical factor in the experimental design. In this study, YOLOv9-e was chosen as a good representative model. Figure 1: YOLO performance comparison for object detection on COCO dataset [14]. Having a good predictive deep learning model requires a large amount of data, which is a well-known fact. Manually collecting and annotating data can be a very costly process during project implementation. In parallel to advancements in object detection, generative artificial intelligence is emerging as an important tool across various fields. One of the sectors where generative AI can be applied is agriculture, particularly in enhancing productivity and sustainability. Technologies like Digital Twin (DT) and generative AI can simulate farming conditions, supporting better decision-making and resource use [15]. One of the applications is crop disease diagnosis, where Generative Adversarial Networks (GANs) create synthetic images to improve detection models. This helps address data limitations and enhances early disease identification. Yield prediction and plant monitoring also benefit from AI, enabling farmers to estimate production and manage resources more effectively [16]. Generative AI also supports data enrichment by filling gaps in agricultural datasets, improving insights for farmers and policymakers. By generating synthetic data for soil analysis and climate trends, AI models become more adaptable to different farming conditions [17,18]. One of the main focuses of our study is the generation of highly realistic images, comparable to those captured in real life. FLUX.1 [19] demonstrates advantages over other state-of-the-art text-to-image models like Ideogram2.0, DALL-E 3, Stable Diffusion 3, Midjourney, excelling in structured output generation, realism and domain-specific tasks such as scientific and medical imaging [20]. While Midjourney and Stable Diffusion 3 might generate aesthetically appealing images, FLUX.1 is better at maintaining logical consistency, structured layouts and accurate text generation, making it more versatile for applications beyond artistic renderings. In this paper, we explore the feasibility of developing a highly accurate predictive AI model for poultry monitoring using synthetic data. We investigate how synthetic data can be effectively combined with real data to enhance deep learning model performance while reducing reliance on extensive real-world image collection. This study was conducted on a single farm under controlled conditions, which will be further explained in the following sections.