Deep Learning for Agar Plate Analysis: Predicting Microbial Cluster Counts

Abstract

Manual analysis of agar plates remains a bottleneck in microbiology, hindering automation efforts. This study investigates the feasibility of using machine learning for automated microbial cluster count detection from agar plate images. We employed various methods, including elbow detection (baseline) and supervised learning models (Support Vector Regression, Simple CNN, XGBoost, Random Forest, pre-trained VGG, and pre-trained Inceptionv3). The results demonstrate that machine learning models significantly outperform the baseline, achieving lower prediction errors and higher accuracy in identifying the correct number of clusters. Notably, both pre-trained VGG and InceptionV3 achieved strong performance, highlighting the effectiveness of transfer learning for this task. InceptionV3 exhibited the lowest error rates overall. This study establishes a foundation for developing robust automated systems for quantifying microbial growth, potentially streamlining workflows and improving efficiency in microbiological research and clinical settings.