Air-Sense: Enhancing Crop Yield and Quality through Integrated IoT-based Air Quality Monitoring System

Clicks: 1
ID: 313081
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #635 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Air quality has a significant effect on crop production and quality. Crops are affected when they are exposed to certain air pollutants. The effect might range from visible spots on the leaves to reduced growth and yield, and plant’s early maturity. The degree of the damage was determined by the concentration of the specific pollutants and several other variables. This paper presents Air-Sense that is an IoT-base real-time air quality monitoring system. It is not only monitoring the quality of air and weather parameters around the crop but also deter-mine its effect on the crop leaves. Air-Sense monitors four gases: hydrogen gas (H2), Carbon Monoxide (CO), Ozone (O3) and Carbon dioxide (CO2). The dust particles (PM2.5), s air temperature, soil moisture and humidity are also determined with this system. The system has been evaluated on the cotton crop by measuring leaf chlorophyll concentration with the help of the image processing model with an average validation accuracy of 93.47% based on an image dataset of 2000 healthy and infected cotton leaves. This system will enable farmers to monitor their fields air quality and make timely decisions about the best suitable sowing location for crops to get better yield and quality.
Reference Key
imported_1777058900_69ebc454d95be Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shahid Iqbal
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/300
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.