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

Favour Adenugba; Sanjay Misra; Rytis Maskeliūnas; Robertas Damaševičius; Egidijus Kazanavičius; Favour Adenugba; Sanjay Misra; Rytis Maskeliūnas; Robertas Damaševičius; Egidijus Kazanavičius

doi:10.3934/mbe.2019273

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 5490-5503. doi: 10.3934/mbe.2019273

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Research article Special Issues

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

1.
Department of Electrical and Information Engineering, Covenant University, Ota 0123, Nigeria
2.
Department of Computer Engineering, Atilim University, Incek 06836, Ankara, Turkey
3.
Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania
4.
Department of Software Engineering, Kaunas University of Technology, Kaunas, Lithuania
5.
Centre of Real Time Computer Systems, Kaunas University of Technology, Kaunas, Lithuania

Received: 21 December 2018 Accepted: 30 May 2019 Published: 13 June 2019

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.
- smart irrigation,
- smart agriculture,
- Internet of Everything,
- Internet-of-Things,
- neural networks,
- decision support
Citation: Favour Adenugba, Sanjay Misra, Rytis Maskeliūnas, Robertas Damaševičius, Egidijus Kazanavičius. Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5490-5503. doi: 10.3934/mbe.2019273

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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.

References

[1]	B. N. Fantu, B. Guush, M. Bart, et al., Agricultural Transformation in Africa? Assessing the Evidence in Ethopia, World Dev., 105 (2018), 286–298.
[2]	S. Trilles, J. Torres-Sospedra, Ó. Belmonte, et al., Development of an open sensorized platform in a smart agriculture context: A vineyard support system for monitoring mildew disease, Sustain. Comput. Infor., (2019), in press.
[3]	Intergovernmental Panel on Climate Change, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC (2014), Geneva, Switzerland, 151 pp., [online] Available from: https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf.
[4]	A. M. García, I. F. García, E. C. Poyato, et al., Coupling irrigation scheduling with solar energy production in a smart irrigation management system, J. Clean. Prod., 175 (2018), 670–682.
[5]	M. Kala, U. Sadrul and B. Steven, Solar photovoltaic water pumping-opportunities and challenges, Renew. Sust. Energ. Rev., 4 (2008), 1162–1175.
[6]	European Commission, Overview of CAP Reform 2014–2020, December 2013, [online] Available from: http://ec.europa.eu/agriculture/policy-perspectives/policy-briefs/05_en.pdf.
[7]	S. Biswajit, Green Computing, Int. J. Comput. Trends Technol., 14 (2014), 46–50.
[8]	S. Murugesan, Harnessing green IT: Principles and practices, IT Prof., 10 (2008), 24–33.
[9]	E. Okewu, S. Misra, R. Maskeliunas, et al., Optimizing green computing awareness for environmental sustainability and economic security as a stochastic optimization problem, Sustainability, 9 (2017), 1857.
[10]	E. Okewu, S. Misra, L. Fernandez-Sanz, et al., An e-environment system for socio-economic sustainability and national security, Probl. Ekorozw., 13 (2018), 121–132.
[11]	A. C. Orgerie, Green Computing and Sustainability, Journées scientifiques, 15, (2016), 23–27.
[12]	A. Al-Zamil and A. K. J. Saudagar, Drivers and challenges of applying green computing for sustainable agriculture: A case study, Sustain. Comput. Infor., (2018), in press.
[13]	A. Mansur, H. Ghassan, S. A. Syed, et al., A review of solar-powered water pumping systems, Renew. Sust. Energ. Rev., 87 (2018), 61–76.
[14]	N. Mehdi, M. Peyman, N. Mohammad, et al., Techno-economic feasibility of off-grid solar irrigation for a rice paddy in Guilan province in Iran: A case study, Sol. Energy, 150 (2017), 546–557.
[15]	P. E. Campana, H. L. Li and J. Y. Yan, Techno-economic feasibility of the irrigation system for the grassland and farmland conservation in China: Photovoltaic vs. wind power water pumping, Energ. Convers. Manage., 103 (2015), 311–320.
[16]	P. E. Campana, H. L. Li and J. Y. Yan, Dynamic modelling of a PV pumping system with special consideration on water demand, Appl. Energy, 112 (2013), 635–645.
[17]	Z. Gu, Z. Qi, L. Ma, et al., Development of an irrigation scheduling software based on model predicted crop water stress, Comput. Electron. Agric., 143 (2017), 208–221.
[18]	R. López-Luque, J. Reca and J. Martínez, Optimal design of a standalone direct pumping photovoltaic system for deficit irrigation of olive orchards, Appl. Energy, 149 (2015), 13–23.
[19]	G. Vellidis, M. Tucker, C. Perry, et al., A real-time wireless smart sensor array for scheduling irrigation, Comput. Electron. Agric., 61 (2008), 44–50.
[20]	T. Ojha, S. Misra and N.S. Raghuwanshi, Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges, Comput. Electron. Agric., 118 (2015), 66–84.
[21]	B. Keswani, A. G. Mohapatra, A. Mohanty, et al., Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms, Neural Comput. Appl., 31(S1) (2018), 277–292.
[22]	S. T. Oliver, A. González-Pérez and J. H. Guijarro, An IoT proposal for monitoring vineyards called SEnviro for agriculture, Proceedings of 8th International Conference on the Internet of Things, (2018), 20. ACM.
[23]	S. A. M. Varman, A. R. Baskaran, S. Aravindh, et al., Deep learning and IoT for smart agriculture using WSN, IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, (2018).
[24]	A. Goap, D. Sharma, A. K. Shukla, et al., An IoT based smart irrigation management system using machine learning and open source technologies, Comput. Electron. Agric., 155 (2018), 41–49.
[25]	T. Kashiwao, K. Nakayama, S. Ando, et al., A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency, Appl. Soft Comput., 56 (2017), 317–330.
[26]	O. Adeyemi, I. Grove, S. Peets, et al., Dynamic neural network modelling of soil moisture content for predictive irrigation scheduling, Sensors, 18 (2018), 3408.
[27]	L. Huang, L. Chen, Q. Wang, et al., Regional short-term micro-climate air temperature prediction with CBPNN, E3S Web of Conferences, 53 (2018).
[28]	L. T. Yang, B. Di Martino and Q. Zhang, Internet of Everything, Mob. Inf. Syst., (2017).
[29]	S. R. Barkunan, V. Bhanumathi and J. Sethuram, Smart sensor for automatic drip irrigation system for paddy cultivation, Comput. Electr. Eng., 73 (2019), 180–193.
[30]	C. Chang and K. Lin, Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme, Robotics, 7 (2018), 38.
[31]	C. Corbari, R. Salerno, A. Ceppi, et al., Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling, Agric. Water Manag., 212 (2019), 283–294.
[32]	W. Difallah, K. Benahmed, B. Draoui, et al., Implementing wireless sensor networks for smart irrigation, Taiwan Water Conservancy, 65 (2017), 44–54.
[33]	S. Geetha and R. Sathya Priya, Smart agriculture irrigation control using wireless sensor networks, GSM and android phone, Asian J. Inf. Technol., 15 (2016), 3780–3786.
[34]	A. Goap, D. Sharma, A. K. Shukla, et al., An IoT based smart irrigation management system using machine learning and open source technologies, Comput. Electron. Agr., 155 (2018), 41–49.
[35]	N. Hema and K. Kant, Cost-effective smart irrigation controller using automatic weather stations, Int. J. Hydrol. Sci. Technol., 9 (2019), 1–27.
[36]	C. Kamienski, J. Soininen, M. Taumberger, et al., Smart water management platform: IoT-based precision irrigation for agriculture, Sensors, 19 (2019), 276.
[37]	S. Katyara, M. A. Shah, S. Zardari, et al., WSN based smart control and remote field monitoring of Pakistan's irrigation system using SCADA applications, Wireless Pers. Commun., 95 (2017), 491–504.
[38]	O. Abayomi-Alli, M. Odusami, D. Ojinaka, et al., Smart-Solar Irrigation System (SMIS) for Sustainable Agriculture, International Conference on Applied Informatics, ICAI 2018, (2018), 198–212.
[39]	A. G. Mohapatra, S. K. Lenka and B. Keswani, Neural network and fuzzy logic based smart DSS model for irrigation notification and control in precision agriculture, P. Natl. A. Sci. India A, 89 (2019), 67–76.
[40]	M. S. Munir, I. S. Bajwa, M. A. Naeem, et al., Design and implementation of an IoT system for smart energy consumption and smart irrigation in tunnel farming, Energies, 11 (2018), 3427.
[41]	X. Fan, W. Wei, M. Wozniak, et al., Low energy consumption and data redundancy approach of wireless sensor networks with bigdata, Inf. Technol. Control, 47 (2018), 406–418.
[42]	A. Venčkauskas, N. Jusas, E. Kazanavičius, et al., An energy efficient protocol for the internet of things, J. Electr. Eng., 66 (2015), 47–52.
[43]	W. Wei, Z. Sun, H. Song, et al., Energy balance-based steerable arguments coverage method in WSNs, IEEE Access, 6 (2018), 33766–33773.
[44]	C.M Bishop, Neural networks for pattern recognition, Oxford University Press, 1995.
[45]	Z. Boger and H. Guterman, Knowledge extraction from artificial neural network models, IEEE Systems, Man, and Cybernetics Conference, 4 (1997), 3030–3035.
[46]	N. Srivastava, G. Hinton, A. Krizhevsky, et al., Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15 (2014), 1929–1958.

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