Using a Digital Camera Combined With Fitting Algorithm and T-S Fuzzy Neural Network to Determine the Turbidity in Water

Abstract

We developed a method using a digital camera combined with fitting algorithm and T-S fuzzy neural network (T-S fnn) to measure the water turbidity accurately and efficiently. A turbidity-measuring device and image-processing software based on the common camera were designed to obtain the image of a standard solution after a constant light source passed through the sample and the RGB values, Lab values corresponding to the image were obtained. These RGB values were used as the input of the fuzzy neural network prediction model, the corresponding standard turbidity values were used as the output, and a camera-based fuzzy neural network turbidity measurement method was established. Simultaneously, the standard curve of turbidity measurement is established by fitting the turbidity using color component and color difference. The proposed method was applied to the measurement of standard turbidity solution and the results were compared with those of turbidmeter. The accuracy of the fuzzy neural network and the fitting algorithm is higher than that of the turbidimeter, and the accuracy of the fuzzy neural network method is the highest, the measurement error was only ±0.89%, and the accuracy much higher than that of an ordinary turbidimeter. By comparing the independent sample t-tests of the actual water samples, the fuzzy neural network method had the same trend as the turbidimeter and there was no significant difference of the results. The water turbidity measurement with the camera-T-S fuzzy neural network assembly can be applied to the actual water samples. This method can replace the traditional photoelectric detection method to measure turbidity, and it can reduce measurement error and cost. It may be used in environmental detection, biomedicine, and other fields.