Integrating Image Processing and Convolution Neural Networks for Water Quality Detection

This study proposes a water quality detection system that combines image processing and Convolutional Neural Network (CNN) models to accurately identify and classify water quality based on visual features. The quality of water has significant impacts on a variety of areas, such as the survival of aquatic creatures, agricultural irrigation, and human health. It is also a crucial factor in global economic development. Therefore, there is a great importance to develop a monitoring system that is simple, quick, low-cost, and reliable. There are certain drawbacks associated with using automatic water quality sensors, including their high cost and the challenges involved in maintaining them. Water colour is a crucial indicator of water quality in lakes or ponds, as it reflects the unique characteristics of the water. The proposed system captures images of water bodies and extracts features such as color, texture, and turbidity, using image processing techniques. These features are used as input to the trained CNN models for water quality prediction and classification. The proposed system is evaluated using a real-world water quality dataset, and the results demonstrate that the proposed system achieves great precision in water quality detection. The system's ability to detect visual anomalies in water quality can provide early warning signals for potential health and environmental hazards.