Drinking Water Monitoring: Computer Vision Kit for Early E.coli Detection

Mansoor Khan; Samad Riaz; Gul Muhammad Khan

Authors

Mansoor Khan
Samad Riaz CISNR, UET Peshawar
Gul Muhammad Khan

Keywords:

Water Quality, Escherichia coli, Computer Vision

Abstract

This work presents an easy-to-use and accurate method to find up to 1 coliform unit (CFU) of a pathogenic bacterium i.e., Escherichia coli (E. coli) in 100ml of drinking water in 6-8 Hours of the incubation period. A larger number of CFUs is easy to detect and incubation time is reduced to 5-7 Hours for the testing samples containing more than 20 CFUs. Normally in laboratories up to 1 ml of a water sample is spread on an endo agar medium and incubated for about 24 Hours, and the E. coli coliform in metallic green color becomes visible through the naked eye. Which has a limitation of finding 1 CFU in just 1 ml of water and a limitation of a large amount of time. In the proposed work Membrane filtration method is used for experiments and a microscopic camera with deep learning algorithms i.e., yolov5 and yolov8 is used for the early detection and counting of E. coli colonies. This system is generalized on the field data of 8k images taken from different cities' water samples in Pakistan. Yolov5s model achieved a mean average precession (mAP@0.5) of .949, while the latest release version yolov8 achieved mAP@0.5 of 0.950. An automatic imagery system is developed that takes the images just by placing a petri dish in it processes those images through Raspberry Pi, and shows the detected colonies on the screen, while remote users can use a low-cost microscopic camera manually with a developed mobile application.

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