Resource Management in Agriculture through IoT and Machine Learning

Clicks: 1
ID: 313160
2023
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 #661 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
In light of agriculture's substantial contribution to global freshwater usage, the imperative for sustainable water resource management is paramount. This study introduces a cutting-edge smart irrigation system, employing IoT technology and machine learning algorithms, to optimize agricultural water productivity. A real-time IoT network, comprising soil moisture sensors and microcontroller gateways, captures field data, which is subsequently transmitted to a cloud-based platform. Data-driven predictive models leveraging Support Vector Machines (SVM), Artificial Neural Networks (ANN), and regression tree and LiNGAM algorithms are constructed to accurately anticipate daily crop water requirements. By seamlessly integrating sensor data with predictive modeling, the system precisely adjusts irrigation levels to align with specific crop demands, initiating automated scheduled irrigation events. This research article focusing on increase in water utalizaton efficiency compared to conventional timer-based irrigation methods by maintain pH value and dissolved oxygen in industrial water. The presented data-centric IoT approach represents a significant advancement in agricultural water management, holding substantial promise for sustainable and precise irrigation practices amid the backdrop of global water scarcity challenges.
Reference Key
imported_1777059485_69ebc69d065f8 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Asia Rehman
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/314
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.