Analysis of Artificial Vision Techniques for Implementation of Virtual Instrumentation System to Measure Water Turbidity

被引:0
作者
Guapacho J.J. [1 ]
Guativa J.A.V. [1 ]
Baquero J.E.M. [1 ]
机构
[1] Faculty of Basic Sciences and Engineering, Universidad de los Llanos, Villavicencio
关键词
Artificial vision; image processing; turbidity; virtual instrumentation; water quality;
D O I
10.25103/jestr.144.20
中图分类号
学科分类号
摘要
This paper presents the results of research framed in sustainable development goal number 6 called Clean water and Sanitation. Through research, it was found that water quality can be determined by measuring its turbidity, research objective was to design a measurement system which would allow to measure turbidity level in different samples using digital image analysis. This work was divided into different phases, starting with an image capture of the samples illuminated with LED light sources of different colors and different angles. Second instance, the processing and analysis of the captured images was carried out, and finally, with data obtained, mathematical model was obtained, it would allow the system to be designed. Study allowed observing the qualities and LED light depending behavior on its wave frequency and location from which the photographic shot is taken. Results obtained allowed finding a correlation between 0.95 and 1.0. Likewise, it was found that blue colored LED light with capture angle from the side offered the best correlation, therefore the most optimal option for the measurement was determined. © 2021 School of Science, IHU. All Rights Reserved.
引用
收藏
页码:161 / 168
页数:7
相关论文
共 22 条
  • [21] Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture
    Hu, Wu-Chih
    Chen, Liang-Bi
    Wang, Bo-Hao
    Li, Guo-Wei
    Huang, Xiang-Rui
    IEEE SENSORS JOURNAL, 2024, 24 (03) : 3811 - 3821
  • [22] Analixity: An open source, low-cost analysis system for the elevated plus maze test, based on computer vision techniques
    Gonzalez-Gaspar, Patricia
    Macias-Carballo, Monserrat
    Cadena-Mejia, Teresa
    Landa-Jimenez, Miguel A.
    Montes-Gonzalez, Fernando M.
    Leonor Lopez-Meraz, Maria
    Beltran-Parrazal, Luis
    Morgado-Valle, Consuelo
    BEHAVIOURAL PROCESSES, 2021, 193