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

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Date

2019

Journal Title

Journal ISSN

Volume Title

Publisher

Amer inst Mathematical Sciences-aims

Open Access Color

GOLD

Green Open Access

Yes

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OpenAIRE Views

Publicly Funded

No
Impulse
Top 1%
Influence
Top 10%
Popularity
Top 1%

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Journal Issue

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.

Description

Misra, Sanjay/0000-0002-3556-9331; Maskeliunas, Rytis/0000-0002-2809-2213; Damaševičius, Robertas/0000-0001-9990-1084; Kazanavicius, Egidijus/0000-0002-4192-3042; Adenugba, Favour/0000-0003-0614-7188

Keywords

smart irrigation, smart agriculture, Internet of Everything, Internet-of-Things, neural networks, decision support, Renewable energy, decision support, smart agriculture, Agricultural engineering, Population, Environmental science, smart irrigation, Engineering, Sociology, Artificial Intelligence, QA1-939, FOS: Electrical engineering, electronic engineering, information engineering, Climate change, internet-of-things, Machine Learning Methods for Solar Radiation Forecasting, Demand Response in Smart Grids, Electrical and Electronic Engineering, Environmental resource management, Irrigation, Biology, Water Science and Technology, Demography, Ecology, Agriculture, artificial intelligence, neural networks, FOS: Sociology, Integrated Management of Water, Energy, and Food Resources, Sustainability, FOS: Biological sciences, Electrical engineering, Computer Science, Physical Sciences, Environmental Science, internet of everything, TP248.13-248.65, Mathematics, Biotechnology, Forecasting

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Fields of Science

02 engineering and technology, 0202 electrical engineering, electronic engineering, information engineering

Citation

WoS Q

Q2

Scopus Q

Q2
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OpenCitations Citation Count
49

Source

Mathematical Biosciences and Engineering

Volume

16

Issue

5

Start Page

5490

End Page

5503

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Citations

Scopus : 56

PubMed : 4

Captures

Mendeley Readers : 273

SCOPUS™ Citations

56

checked on Feb 08, 2026

Web of Science™ Citations

33

checked on Feb 08, 2026

Page Views

2

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OpenAlex FWCI
5.99109208

Sustainable Development Goals

2

ZERO HUNGER
ZERO HUNGER Logo

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

13

CLIMATE ACTION
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17

PARTNERSHIPS FOR THE GOALS
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