Development of Image Processing Incorporated Real-Time Smart Water Quality Monitoring System to Detect Algae and Debris for a Sustainable Environment

Access to clean and safe drinking water is critical to human well-being while ensuring water quality remains an ongoing challenge. Despite the water board's best attempts at filtration, the potential for contamination remains due to factors such as inappropriate storage, deteriorating pipelines, or unintentional mixing during transit to houses. To address this issue, this study introduces an image processing incorporated real-time smart water quality Monitoring system to detect Algae and debris for a sustainable environment. This micro controller-based device consists of sensors, cameras, and image processing technologies to deliver real-time data for analysis. An object detection and localization model were trained using YOLO V5 architecture with 73.4% accuracy to assess the existence of Algae and debris. The developed user-friendly and accurate portable system will undoubtedly benefit the developing field of water quality monitoring and safeguarding public health. With further technological enhancements, the developed method will enable the assessment of numerous qualitative and quantitative parameters of water to contribute to environmental sustainability.