Assessment of Bactericidal Efficacy of Seaside Water Samples and Automated Detection of Sulfate-Reducing Bacteria Using Computer Vision Models

Abstract

This study examines the presence of sulfate-reducing bacteria, specifically Desulfovibrio desulfuricans, in seawater samples from four coastal sites of the Caspian Sea: Neftchala, Bilgah, Sumgayit, and Pirallahi. The analysis aims to assess microbial dynamics and environmental factors across these locations. sulfate-reducing bacteria concentrations were evaluated using microbiological techniques, revealing significant contamination in the Pirallahi region, likely due to industrial activities. A computer vision model based on the “You only Look Once” algorithm was developed to enhance detection accuracy, automating the identification of Sulfate-Reducing Bacteria infected ampoules. The model demonstrated high accuracy with a mean Average Precision of 99.5 %, precision of 91.6 %, and recall of 98.7 %. This study highlights the potential of combining microbiological assessments with automated detection techniques to improve environmental monitoring. It offers insights into the relationship between industrial pollution and microbial contamination in sensitive marine ecosystems.