## 5 Conclusions  
This work explores a SAM-based Copy-Paste data augmentation method for agar dish images to address the challenges of gathering and annotating large datasets for training neural networks. By using the Segment Anything Model (SAM) to cut out microbial colonies and generate synthetic images, the method enables rapid dataset expansion with automatic annotations. However, the SAM struggles with blurry objects, often requiring manual sorting to ensure accurate colony cutouts.  
Our comprehensive experiments highlight the substantial impact of enhancements in synthetic data generation. Beginning with unfiltered SAM-generated cutouts, pre-training solely on synthetic data raised AP\({}^{50}\) from 89.0 to 91.2 and mAP from 56.4 to 58.9, demonstrating significant gains compared to models trained exclusively on 1000 real images. Introducing overlapping colony pairs further boosted the mAP to 60.5, emphasizing the importance of capturing realistic colony interactions. The final augmentation process also included color differentiation to make synthetic images appear more realistic. While this improved visual quality, it did not significantly impact model performance.  
Our best-performing fine-tuned model, utilizing only 1000 real images, achieved an mAP of 60.6, a mere \(-4.3\) behind the baseline trained on 5241 real images. This result underscores the efficiency of the SAM-based Copy-Paste augmentation in achieving near-parity performance with a fraction of the labeled data. Furthermore, our method outperformed a substantially larger model, yielding a \(+5.0\) mAP improvement with just 50 real images. This demonstrates that a strategic combination of synthetic data augmentation and lightweight models can significantly enhance both detection accuracy and computational efficiency. These findings illustrate the potential of our SAM-based Copy-Paste techniques for scalable, automated dataset generation in colony detection, providing a foundation for future refinements such as dynamic opacity, enhanced style transfer techniques, and adaptive augmentation strategies tailored to specific microbial morphologies.  
In this work, our primary focus was colony detection in the context of hygiene monitoring, where the main objective was to identify all potential contaminations to evaluate environmental purity. Although characteristics such as colony shape, color, and viability are important in other applications, they are secondary considerations for this specific use case. Future research could enhance our approach by incorporating advanced morphological analyses, enabling more comprehensive colony characterization and expanding the applicability of our method to a wider range of microbiological and industrial scenarios.  
Conceptualization, K.M., N.E. and O.W.; methodology, K.M. and N.E.; software, K.M.; validation, K.M., N.E. and O.W.; formal analysis, K.M.; investigation, K.M.; resources, K.M.; data curation, K.M. and N.E.; writing--original draft preparation, N.E. and L.R.; writing--review and editing, N.E., L.R. and O.W.; visualization, N.E., L.R. and O.W.; supervision, N.E., L.R. and O.W.; project administration, N.E. and O.W.; funding acquisition, O.W. All authors have read and agreed to the published version of the manuscript. This work was supported by funding from the Federal Ministry of Education and Research Germany in the project M\({}^{2}\)Aind-DeepLearning (13 FHS108 IA). Additional funding was provided by the German Research Foundation under grant number INST874/9-1 and by the Albert and Anneliese Konanz Foundation.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. AGAR-data were obtained from [4] and are available at [[https://agar.neurosys.com/](https://agar.neurosys.com/)]([https://agar.neurosys.com/](https://agar.neurosys.com/)) (accessed on 12 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.