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Traditional methods for testing water quality are usually slow, expensive, and require advanced lab equipment, which makes them hard to use in rural or less developed areas. This paper introduces an AI-based method for checking if water is clean using a smartphone microscope. The system uses deep learning combined with mobile microscopy to take pictures of water samples and then uses a Convolutional Neural Network (CNN) to determine if the water is clean or polluted. This approach allows for quick, on- site testing without needing special skills or equipment, which helps improve public health by lowering the risk of water-related diseases. The paper also looks at existing research and shows how AI and portable imaging can be used to create affordable, scalable, and effective solutions for monitoring water quality.
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Cite Article:
"AI-Driven Clean Water Quality Detection via Mobile Microscopy", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 10, page no.b179-b188, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510121.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator