Measurement of Fe and Mn concentrations using image processing techniques based on color intensity approach

¹ Master Program of Environmental Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, Indonesia
² Department of Environmental Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, Indonesia
³ Doctoral Program of Environmental Engineering, Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Jl. Ganesha No. 10, Bandung 40132, Indonesia
⁴ Department of Environmental Engineering, Faculty of Engineering, President University, Jl. Ki Hajar Dewantara, Jababeka, Cikarang, Bekasi 17550, Indonesia

^* Corresponding author: 25321314@mahasiswa.itb.ac.id

Abstract

Water quality monitoring is an important activity to create a good environment quality with clean and healthy water sources. Various monitoring methods that are generally used such as spectrometry-based instruments certainly have various limitations, such as expensive, requiring a lot of reagents, sensitive instruments, and takes quite a long to get measurement results. Due to the development of population growth and the increasing of water pollutant, water quality monitoring technology that cheap, practical, quick and accurate is important to be made. The main subject in this work was to develop a water quality monitoring method based on multiparameter image processing techniques. This method utilizes the approach of color intensity, light, and number/size/shape of particles. This work will be focus on iron (Fe) and manganese (Mn) concentration measurement by color intensity approach performed using ColorSlurp and Microsoft Excel in its RGB (Red, Green, Blue) matrices. The correlation between the parameter concentration and color intensity was obtained by transforming the RGB into greyscale intensity (GI) value. A linear response was observed in the Fe concentration range 0 to 2.4 mg L⁻¹ with the highest R² = 0.971, and in the Mn concentration range between 0 to 1.6 mg L⁻¹ with the highest R² = 0.9432. This work demonstrates that image processing techniques provide a great promise as water quality monitoring method.