DSpace Repository

Automated Bacteria Colony Counting on Agar Plates Using Machine Learning

Show simple item record

dc.contributor.author Gupta, Rajiv
dc.date.accessioned 2024-09-26T09:36:07Z
dc.date.available 2024-09-26T09:36:07Z
dc.date.issued 2021-10
dc.identifier.issn https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EE.1943-7870.0001948
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/15711
dc.description.abstract The identification of E. coli bacteria is critical for the prevention of health risks. According to EPA-approved gold standard methods, 24–48 h are required to count viable cells in water. Manual counting of viable bacteria colonies on agar plates is time-consuming and can be prone to human error. The method requires experts to identify and count colonies on agar plates using a microscope. Hence, the bacterial counting procedure must be automated in order to decrease error. The main objective of this study was to develop an automatic system for bacteria colony counting. A total of 1,301 groundwater samples were collected from eight districts in Rajasthan, India, for a field investigation. The results were validated using artificial intelligence (AI) methods on this experimental data set. We automated the process of E. coli bacteria identification using a convolutional neural network (CNN). We developed a smartphone application for the rapid detection of E. coli bacteria on agar plates using CNN. We also automated the process of bacteria colony counting using faster region-based convolutional neural network (R-CNN) to overcome manual cell counting process limitations. A graphical user interface (GUI) application was created to rapidly count bacteria colony–forming units on agar plates using faster R-CNN. The developed faster R-CNN model achieved an overall accuracy of 97% and an error (loss) of 0.10. The performance of the CNN and faster R-CNN models was validated using F-score, precision, sensitivity, and accuracy statistical measures. The comparative analysis showed that the faster R-CNN model is reliable and effective in E. coli cell counting. The study developed a system for identifying and counting viable cells of E. coli bacteria in water that can be used to forecast hotspots of water contamination. en_US
dc.language.iso en en_US
dc.publisher ASCE en_US
dc.subject Civil Engineering en_US
dc.subject Bacteria en_US
dc.subject R-CNN model en_US
dc.title Automated Bacteria Colony Counting on Agar Plates Using Machine Learning en_US
dc.type Article en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account