International Journal of Advances in Electronics and Computer Science ( IJAECS )
A highly rated peer reviewed monthly International Journal
Editor-in-Chief : Dr. P. Suresh
Contact Person : Technical Editor
Contact Mail : [email protected]  
Current Issue : Volume-11,Issue-12  ( Dec, 2024 ) View More
Journal Impact Factor : 2.68 View More

Journal Info
Publisher:IRAJ
ISSN (p): 2394-2835
Issues /Year :12
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Paper Detail


Paper Title
Harvesting Data Iot-Enabled Air Pollution Monitoring for Smart Agriculture Optimization

Abstract
This study examines the convergence of agriculture, Internet of Things (IoT) technologies, and air pollution mitigation strategies. The use of IoT devices in modern agriculture has become increasingly common, providing real-time analytics and data-driven insights into aspects of agricultural practices. The real time analytics of air pollution will help the farmers to derive strategies for smart agriculture. Farmers now have access to specific and detailed information about various air pollutants in the region, including PM10, PM2.5, SO2, NO2, CO, NH3, and O3. The classification into categories like Good, Satisfactory, Moderate, Poor, Very Poor, and Severe based on AQI. This will help farmers understand the severity of air pollution in their area. The proposed Deep Learning based EIDMLP Air pollution monitoring system will guide the farmers in a more competent way. With accurate air quality data, farmers can make more informed decisions about the types of crops to cultivate. Certain crops may be more resilient to specific levels of air pollution, and farmers can adjust their crop selection based on the AQI classification to optimize yields and reduce potential losses. The proposed Deep Learning based Air pollution monitoring system will guide the farmers in a more competent way. The ensemble based Incremental Deep Multiple Layer Perceptron model is proposed for real time air pollution analytics. The proposed model has achieved accuracy of 98.6%, and the processing time for feature selection and classification has achieved 0.056 seconds. The novelty of the proposed model lies on the efficacy of handling real time analytics of air pollution dataset with an incremental deep learning approach, which has proved enhanced accuracy and lesser processing time. Keywords - IoT, Air Pollution, Analytics, AQI, Agriculture


Author - Renuka Devi D, Swetha Margaret Ta, Diana Judith I, Rajalakshmi S

Published : Volume-11,Issue-3  ( Mar, 2024 )



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