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

Internet of Everything Internet-of-Things decision support neural networks smart agriculture smart irrigation

Journal

Mathematical biosciences and engineering : MBE
ISSN: 1551-0018
Titre abrégé: Math Biosci Eng
Pays: United States
ID NLM: 101197794

Informations de publication

Date de publication:
13 06 2019
Historique:
entrez: 11 9 2019
pubmed: 11 9 2019
medline: 11 9 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31499722
doi: 10.3934/mbe.2019273
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

5490-5503

Auteurs

Favour Adenugba (F)

Department of Electrical and Information Engineering, Covenant University, Ota 0123, Nigeria.

Sanjay Misra (S)

Department of Electrical and Information Engineering, Covenant University, Ota 0123, Nigeria.
Department of Computer Engineering, Atilim University, Incek 06836, Ankara, Turkey.

Rytis Maskeliūnas (R)

Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania.

Robertas Damaševičius (R)

Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania.

Egidijus Kazanavičius (E)

Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania.

Classifications MeSH