This poster, presented at PDA 2026, illustrates an AI-based system for early detection and tracking of microbial colony growth in environmental monitoring. Using automated incubation, imaging, and object detection models, the system analyzes agar plates over time and tracks colonies across multiple views. Results show perfect agreement with expert microbiologists (100% sensitivity and specificity) and high quantitative accuracy. The AI minimizes operator variability, ensures reproducibility, and improves workflow efficiency. Overall, it demonstrates a robust and reliable solution for standardized, automated microbiological monitoring.
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