The accurate and efficient analysis of microbial cultures on agar plates is fundamental to diverse biomedical applications. However, existing state-of-the-art machine learning solutions often struggle to generalize to the complexities of real-world samples, such as overlapping colonies, varying lighting, and diverse agar types, due to a critical scarcity of large, annotated datasets. This doctoral thesis addresses these limitations by developing and applying advanced machine learning techniques to the image analysis of agar plates.

A primary challenge tackled is the scarcity of annotated datasets. To overcome this, a novel methodology for generating highly realistic synthetic agar plate images is introduced. This approach demonstrates its significant effectiveness in augmenting real-world data, leading to a substantial enhancement in model performance. For instance, the proposed synthetic data pipeline improved the F1 score for semantic segmentation of microbial colonies from 0.518 to a peak of 0.729, demonstrating a marked improvement in accuracy and generalizability. This foundational segmentation model was further refined and validated in subsequent work, achieving an F1 score of 0.906 on an expanded dataset, thereby providing a highly robust basis for all subsequent analysis.

Furthermore, the thesis advances the analysis beyond simple segmentation by implementing a sophisticated analytical approach for the precise characterization of microbial material. This modular system uses Gaussian Mixture Models (GMMs), with their parameters optimized by genetic algorithms, to cluster pixels based on their morphological features. This automated approach achieves a V-measure of 0.723 under optimal conditions, significantly outperforming a k-means method of 0.545 and removes the need for manual tuning.

This research contributes a comprehensive computational framework that demonstrably advances automated microbial analysis beyond previous methods. The methodologies developed provide a robust foundation for building next-generation automated laboratory systems, promising to accelerate diagnostics, streamline research, and elevate the standard of microbial quantification and characterization.