Determine Water Turbidity by Using Image Processing Technology - Ali Mohammed (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The current study includes the measurement of water turbidity using image processing technology, as this technology is one of the most promising modern technologies due to the development in the field of imaging devices, the possibility of communication with various modern devices among themselves. In this study uses a deep learning method through an image-based convolutional neural network (CNN) to estimate water turbidity. In this way, samples of different values of water turbidity were prepared, which were measured in the laboratory by one of the devices specialized in turbidity in the laboratory, and then they were photographed in a suitable way for the purpose of entering them into the network. The network was created using Python programming, which is easily downloaded and using the (Colab Google) platform. After preparing a program for five levels of turbidity and testing its success, the results of the proposed system were compared with the results of the turbidity scale. The results showed that the performance of the proposed method by adopting five groups of turbidity degrees is good. The categories of water turbidity were detected with an accuracy of 91.6% only due to the accuracy of the imaging as well as the number of images entered.