Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach.

Clicks: 460
ID: 57599
2019
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
Steady

Ranked #1 of 23 articles by views in mathematical biosciences and engineering : mbe

Most read Least read

Bar heights use a square-root scale.

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
Water and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.
Reference Key
adenugba2019smartmathematical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Adenugba, Favour;Misra, Sanjay;Maskeliūnas, Rytis;Damaševičius, Robertas;Kazanavičius, Egidijus;
Journal mathematical biosciences and engineering : mbe
Year 2019
DOI
10.3934/mbe.2019273
URL
Keywords
decision support neural networks smart agriculture internet-of-things internet of everything smart irrigation

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.