Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning

Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associated with traditional detection methods. In recent years, researchers capitalized on this potential by developing segmentation algorithms that were specifically tailored to identify the overgrowth of filamentous bacteria and the risk of sludge bulking. This study investigated the optimization of an artificial intelligence (AI) segmentation model in terms of accuracy metrics and computational requirements. Specifically, three model variables were tested, including training sample size, image resolution, and number of training epochs. The results indicated that larger sample sizes resulted in higher output accuracy up to a certain limit (300 images), beyond which no significant improvements were observed. High image resolution (788 × 530) provided more details for the deep learning model to detect the fine edges between bacteria albeit with significant additional computational requirements. The addition of more training epochs resulted in a minor increase in segmentation accuracy, particularly for thin interconnected filamentous bacteria. Overall, high resolution and epochs did not have a major effect when the sample size was large (300 and 500 images). The findings highlight the optimal balance between model accuracy and computational demands, emphasizing the importance of prioritizing diverse training samples with sufficient sample size. This approach is critical for large-scale implementation, as it enhances the potential of AI to deliver timely and accurate predictions, leading to early warnings of wastewater treatment issues such as sludge bulking.