VLSI-ENABLED AI-BASED DIGITAL IMAGE PROCESSING FRAMEWORK FOR REAL-TIME RIVER WATER QUALITY ASSESSMENT AND TURBIDITY MONITORING

Abstract

Water quality monitoring plays a crucial role in environmental protection, public health, and sustainable water resource management. Traditional water quality assessment methods often involve labor-intensive sampling procedures, costly laboratory analyses, and delayed result generation. To overcome these limitations, this study proposes a VLSI-enabled artificial intelligence and digital image processing framework for efficient river water quality evaluation, with a primary focus on turbidity estimation and monitoring. Standard turbidity solutions were prepared at 5 NTU intervals and validated using a calibrated nephelometer to establish reference datasets. River water samples were collected from different locations, and high-resolution images were captured under varying illumination and background conditions to analyze environmental influences on image acquisition. Using MATLAB, advanced image processing techniques such as image enhancement, color feature extraction, texture analysis, segmentation, edge detection, and pattern recognition were employed to characterize water quality parameters. Furthermore, the study investigates the integration of Very Large-Scale Integration (VLSI) architectures for accelerating image processing operations and enabling low-power, high-speed implementation suitable for embedded environmental monitoring systems. The extracted image features were compared with standard turbidity references to classify water quality levels accurately. Recent developments in machine learning, computer vision, and VLSI-based hardware acceleration for environmental sensing applications were also explored to enhance monitoring performance. Experimental results indicate that the proposed VLSI-assisted image processing approach provides a rapid, cost-effective, and reliable solution for real-time water quality assessment, demonstrating significant potential for deployment in smart environmental surveillance networks, Internet of Things (IoT)-enabled water management platforms, and next-generation sustainable monitoring systems.