Original scientific paper

https://doi.org/10.31803/tg-20200524225359

Detection of Escherichia Coli Bacteria in Water Using Deep Learning: A Faster R-CNN Approach

Hüseyin Yanık orcid.org/0000-0002-4386-5536 ; Mersin University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Çiftlikköy Campus, Faculty of Engineering, E Building, Floor: 3, 33343 Yenişehir/MERSİN, Turkey
A. Hilmi Kaloğlu orcid.org/0000-0002-5384-2776 ; Mersin University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Çiftlikköy Campus, Faculty of Engineering, E Building, Floor: 3, 33343 Yenişehir/MERSİN, Turkey
Evren Değirmenci orcid.org/0000-0001-7750-9719 ; Mersin University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Çiftlikköy Campus, Faculty of Engineering, E Building, Floor: 2, 33343 Yenişehir/MERSİN, Turkey

Abstract

Considering its importance for vital activities, water and particularly drinking water should be clean and should not contain disease-causing bacteria. One of the pathogenic bacteria found in water is the bacterium Escherichia coli (E. coli). In the commonly used method for the detection of E. coli bacteria, the bacteria samples distilled from the water sample are seeded in endo agar medium and the change in the color of the medium as a result of the metabolic activities of the bacteria is examined with the naked eye. This color change can be seen with the human eye in approximately 22 ± 2 hours. In this study, a new bacteria detection scheme is proposed – using deep learning to detect E. coli bacteria both in shorter time and in practical way. The proposed technique is tested with experimentally collected data. Results show that the detection of bacteria can be done automatically within 6-10 hours with the proposed method.

Keywords

deep learning; Escherichia coli; Faster R-CNN; prediction; TensorFlow

Hrčak ID:

243661

URI

https://hrcak.srce.hr/243661

Publication date:

14.9.2020.

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