Certain Investigation on IoT Based Cotton Dust Pollution Monitoring in Textile Plants

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F; Shinyei PPD42NS; and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software.

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Publication Details

Published
2025-04-23
DOI
https://doi.org/10.1109/icict64420.2025.11004887
Primary Topic
Smart Agriculture and AI
Type
article
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article

Certain Investigation on IoT Based Cotton Dust Pollution Monitoring in Textile Plants

S Irfan, P. Sivanesan, J. V. Anand, Trilok Sai Vaddanam
Smart Agriculture and AI
article

Certain Investigation on IoT Based Cotton Dust Pollution Monitoring in Textile Plants

S Irfan, P. Sivanesan, J. V. Anand, Trilok Sai Vaddanam
article en

Abstract

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F; Shinyei PPD42NS; and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software.

Siddhartha Medical College (IN), Sri Ramachandra Institute of Higher Education and Research (IN), SASTRA University (IN)
Openalex Percentile: Top 92%
Smart Agriculture and AI
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