Water samples for turbidity detection

Citation Author(s):
Zahra
Nazemi
Mohamad Faiz
Zainuddin
Iszuanie Syafidza
Che Ilias
Submitted by:
Mohamad Zainuddin
Last updated:
Tue, 09/28/2021 - 23:50
DOI:
10.21227/fnea-8z25
Data Format:
License:
0
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Abstract 

Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera. A total of 71 samples were taken, representing varying magnitudes of nephelometry turbidity unit (NTU) between 0 to 100. In this research, CNN models were used to detect water turbidity. Based on the results, when grayscale images were used, and an NTU of less than 5 are considered images of clean water, the CNN algorithm achieved an accuracy of 0.9811 and a loss of 0.0514. When color images were used and an NTU less than 5 is considered clean water, the CNN algorithm achieved an accuracy of 0.9811 and a loss of 0.0414. The exact process was repeated when grayscale and color images were used, with NTU less than 15 considered as clean water. The performance of Python and R in terms of time required to train a convolutional neural network using Keras was compared. The results found that training a CNN Keras model in Python is faster than R, while the accuracy of the provided model is independent of the programming language

Instructions: 

These are raw images of water samples with specific turbidity values prepared by mixing 1000 NTU Formazine stock solution with pure water. 

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