AI Driven Automatic Detection of Bacterial Contamination in Water : A Review
Chethna Joy, G. Naveen Sundar, D. Narmadha
Abstract
A crucial pillar of our health is clean water. Drinking polluted water, according to the WHO, can cause diseases such as cholera, dysentery, typhoid and polio and is expected to cause 485,000 annual diarrheal deaths. For the treatment & prevention of waterborne diseases, smart & diligent identification of pathogens is therefore very important. The dawn of machine learning and deep learning, powered by AI, has created a benchmark advancement in the field of object detection. This paper analyzes various types of approaches to machine learning and deep learning used in automated bacteria detection and classification. This paper also discusses the role of AI in tracking bacterial pollution in water in real time and automatically detecting it.