Microbiological Water Quality Test Results Extraction from Mobile Photographs
| Autor: | Stéphane Bressan, Ismail Khalil, Ngurah Agus Sanjaya Er, Laure Sioné, Zhang Ruixi, Remmy A. M. Zen, Jifang Xing |
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| Rok vydání: | 2019 |
| Předmět: |
Sanitation
Database Computer science media_common.quotation_subject Feature extraction 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Test (assessment) Water resources Mobile phone 0202 electrical engineering electronic engineering information engineering Citizen science 020201 artificial intelligence & image processing Quality (business) Water quality computer 0105 earth and related environmental sciences media_common |
| Zdroj: | iiWAS |
| Popis: | An emerging and promising approach to achieving access to water and sanitation for all leverages citizen science to collect valuable data on water quantity and quality, which can assist policymakers and water utility managers in sustainably managing water resources. This paper specifically considers water quality data collected using a mobile phone app wielded by citizen-scientists. The citizens use a microbiological water quality test kit to measure E. coli content. The test result is photographed by the citizens and passed on to scientists for interpretation. However, reading the results necessitates a trained scientific eye and is time consuming. This paper therefore puts forward an algorithm that can automatically infer the outcome of the test from a photograph. To this end, we evaluate several image processing and machine learning algorithms for the automatic extraction of results from photographs of microbiological water quality tests. We devise and present a new knowledge and rule-based algorithm and show that it performs satisfactorily. |
| Databáze: | OpenAIRE |
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