AI-Based Detection of Coliform Colonies Using CNN Transfer Learning for Application to Cultured Plate Analysis in Water Quality Research
Pathogenic bacterial contamination of water poses a severe public health risk, particularly in settings with limited laboratory resources. We propose a two-stage artificial intelligence (AI) pipeline for automated detection and classification of coliform colonies on agar plates. In the first stage, a YOLOv8-based detector localizes colonies on full-plate images, eliminating the need for manual annotation. In the second stage, detected colony patches are classified using a convolutional neural network (CNN) trained via transfer learning, where models are first pretrained on a diverse public bacterial colony dataset and subsequently fine-tuned on coliform-specific classification tasks. Across both in-house and public datasets, transfer learning consistently improves classification performance relative to training from scratch. The complete pipeline processes each plate in under five seconds and outperforms classical feature-based baselines, including Histogram of Oriented Gradients, Local Binary Patterns, and Haralick descriptors with conventional classifiers. These results demonstrate the potential of a modular, low-cost AI framework for scalable and accessible microbiological analysis, with future work targeting color-aware models and on-device inference for field deployment.