Automatic Detection and Assessment System of Water Turbidity based on Image Processing

Cheng, En; Zhang, Rongxin; Yuan, Fei

doi:10.11591/telkomnika.v11i3.2299

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Likewise, image processing is used to check the turbidity of the water flowing through the pipe, examine the sludge, dust or debris in the water, and find the floating oil layer that flows with the water inside the pipe [3]- [5]. Therefore, this image processing technique helps to measure the oil turbidity to a level that can be consumed.…”

Section: Introductionmentioning

confidence: 99%

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Palananda

Kimpan

2021

IEEE Access

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In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity. INDEX TERMSImage processing, turbidity level, moving average, coconut oil, Computer vision.

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Section: Introductionmentioning

confidence: 99%

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Palananda

Kimpan

2021

IEEE Access

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“…To know the real condition of the road, the data used can be in the form of digital image data which then process in further processing process. Image processing has been widely used for the identification and detection of objects, which are rice variety identification, biometric personal identification, water turbidity detection, and cotton diseases detection [13][14][15][16]. This technique has been done to classify road damage on digital images by using region split merger and fractal dimension.…”

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confidence: 99%

Classification of Road Damage from Digital Image Using Backpropagation Neural Network

Sutikno

Wibawa

Budiarto

2017

IJ-AI

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One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

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A Review of Turbidity Detection Based on Computer Vision

2018

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Automatic Detection and Assessment System of Water Turbidity based on Image Processing

Cited by 5 publications

References 3 publications

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Turbidity of Coconut Oil Determination Using the MAMoH Method in Image Processing

Classification of Road Damage from Digital Image Using Backpropagation Neural Network

A Review of Turbidity Detection Based on Computer Vision

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