Estimating water turbidity from a smartphone camera

Lina M Lozano Wilches (Imperial College London),* Chotiwat Jantarakasem (Imperial College London), Laure Sioné (Imperial College London), Michael Templeton (Imperial College London), Krystian Mikolajczyk (Imperial College London)
The 33rd British Machine Vision Conference


Water quality monitoring is indispensable for safeguarding human health. One aspect of water quality is turbidity, the measurement of which typically involves on-site water sampling and laboratory analysis, which may be both costly and labour-intensive in the context of developing countries. Alternative portable devices have been developed but they are often inconvenient and require technical expertise. In recent years, smartphone-based solutions have been developed with the aim of bringing turbidimeters to the wider population. However, they rely on additional equipment to create enclosed environments for the sample and the camera to remove ambient light. Therefore, turbidimeters in general require either technical expertise or additional equipment, which has limited their usage, especially in developing countries, where they are most needed. In this paper we introduce a new benchmark with a new task for computer vision. We proposed and evaluated an approach for measuring water turbidity from a picture taken by a smartphone camera without any additional equipment. We follow a simple protocol for taking a picture of a water sample that allows to estimate its turbidity, collect a dataset and design a benchmark for measuring the performance of computer vision methods in this task. Our model is able to determine turbidity in the range of 0 - 40 NTU under controlled lab conditions.



author    = {Lina M Lozano Wilches and Chotiwat Jantarakasem and Laure Sioné and Michael Templeton and Krystian Mikolajczyk},
title     = {Estimating water turbidity from a smartphone camera},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

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