IRO Journals
Journal of Electronics and Informatics

Smart Portable Sensor System for Microplastic Detection in Water

Open Access
Published: 14 October, 2026
Pages: 377-388
  • Bavatharani K. , Bavatharani K.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Janani S. , Janani S.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Leena Sri S. , Leena Sri S.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Preethi Monika K. Preethi Monika K.
    Assistant Professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Assistant Professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
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How to Cite

K., Bavatharani, Janani S., Leena Sri S., and Preethi Monika K. 2026. “Smart Portable Sensor System for Microplastic Detection in Water”. Journal of Electronics and Informatics 8 (4): 377-88. https://doi.org/10.36548/jei.2026.4.005.
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Keywords

Microplastics
Water Quality Monitoring
Optical Sensing
Photodiode Sensor
Embedded Detection System
Threshold-Based Classification
Real-Time Contamination Detection

Abstract

Microplastic contamination is existing as one of the major environmental issues, which needs to be addressed in an efficient manner using economical detection methods. Existing microplastics detection methods use spectroscopic and microscopic approaches to provide accurate results but these methods rely on expensive equipment and sample preparation processes. In the proposed system, LED is used, while the photodiode measures the change in the intensity of transmitted light with the LM358 being the operational amplifier used for signal conditioning. The Arduino Uno serves as the microcontroller, which takes the data input and classifies the level of contamination based on a threshold level. The contamination level is indicated by using an OLED interface and indicator LEDs is used to enable instant interpretation for the users. Experiments carried out using water with various levels of contamination showed reliable sensor operation with quick response time and accurate classification of the contamination level.

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