TY - GEN
T1 - Computer Vision Technique to Improve the Color Ratio in Estimating the Concentration of Free Chlorine
AU - Ruiz-Navarro, José Alonso
AU - Santos-López, Félix Melchor
AU - Portella-Delgado, Jhon Manuel
AU - Santos-de-la-Cruz, Eulogio Guillermo
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Access to drinkable water is a constitutional right in Peru. However, quality water is not universally accessible, particularly in rural areas where neither water disinfection nor quality control are undertaken. It is therefore crucial to develop methods that allow estimates of water quality characteristics, such as free chlorine concentration. This publication proposes a sensing system that estimates free chlorine concentration using a traditional colorimetric method of chemical reaction with N, N-diethyl-p-phenylenediamine and a stationary digital camera. Using image processing, a subset of computer vision, the algorithm quantifies the color of the water sample after chemical reaction with the reagent and, on the basis of that quantification, tries to estimate the free chlorine concentration of the sample. After the quantification, a model is determined using a best-fit polynomial. The resulting polynomial model is then implemented using serverless cloud computing resources from Amazon Web Services, and its results are also hosted using this platform. The model error has a standard deviation of 0.0129 ppm.
AB - Access to drinkable water is a constitutional right in Peru. However, quality water is not universally accessible, particularly in rural areas where neither water disinfection nor quality control are undertaken. It is therefore crucial to develop methods that allow estimates of water quality characteristics, such as free chlorine concentration. This publication proposes a sensing system that estimates free chlorine concentration using a traditional colorimetric method of chemical reaction with N, N-diethyl-p-phenylenediamine and a stationary digital camera. Using image processing, a subset of computer vision, the algorithm quantifies the color of the water sample after chemical reaction with the reagent and, on the basis of that quantification, tries to estimate the free chlorine concentration of the sample. After the quantification, a model is determined using a best-fit polynomial. The resulting polynomial model is then implemented using serverless cloud computing resources from Amazon Web Services, and its results are also hosted using this platform. The model error has a standard deviation of 0.0129 ppm.
KW - Cloud
KW - Free chlorine
KW - Image processing
KW - Model
KW - Serverless
UR - http://www.scopus.com/inward/record.url?scp=85131924936&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-97719-1_7
DO - 10.1007/978-3-030-97719-1_7
M3 - Conference contribution
AN - SCOPUS:85131924936
SN - 9783030977184
T3 - Lecture Notes in Networks and Systems
SP - 127
EP - 141
BT - Advances and Applications in Computer Science, Electronics, and Industrial Engineering - Proceedings of the Conference on Computer Science, Electronics and Industrial Engineering CSEI 2021
A2 - Garcia, Marcelo V.
A2 - Fernández-Peña, Félix
A2 - Gordón-Gallegos, Carlos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Computer Science, Electronics, and Industrial Engineering, CSEI 2021
Y2 - 25 October 2021 through 29 October 2021
ER -