Fog Computing Enabled Hydroponic Farming Systems
DOI:
https://doi.org/10.13052/jmm1550-4646.1842Keywords:
Fog computing, Smart farming, IoT, Data mining, ANNAbstract
Intelligent hydroponic farming that leverages IoT advantages is a pattern of modern farming technology as it not only increases crop productions but also reduces negative impacts from traditional methods. This paper proposed a fog computing enabled hydroponic farming framework that devises low-cost data collection and novel data analysis mechanisms to deliver intelligent farming systems. In this framework, the data from multiple IoT sensors at the garden are collected, filtered and analyzed by artificial neural network (ANN) models deployed at the fog landscapes, while the ANN models are trained in the cloud with a large amount of historical farming data. This approach allows the intelligent models being updated, reducing the communication cost and response time, while utilizing computing resources available on the network edge. The evaluation results on the developed prototype depict the effectiveness and the performance of the proposed approach revealing that it is feasible and ready to be applied in real-world applications.
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S. Ranger, “What is the iot? everything you need to know about the internet of things right now,” 2020.
Y. Rivas-Sańchez, M. Moreno-Pérez, and J. Roldań-Canãs, “Environment control with low-cost microcontrollers and microprocessors: Application for green walls,” 2019.
N. Huong, “Vbf 2018: Investment opportunities in smart agriculture,” Vietnam Investment Review, 2018, https://vir.com.vn, accessed Aug. 2021.
M. Chan, “Big data in the cloud: Why cloud computing is the answer to your big data initiatives,” 2018.
Cisco, “Fog computing and the internet of things: Extend the cloud to where the things are,” 2015.
M. Firdhous, O. Ghazali, and S. Hassan, “Fog computing: Will it be the future of cloud computing?” 2014.
J. Cleland, “World population growth; past, present and futures,” Environmental and Resource Economics, vol. 55, no. 4, pp. 543—554, 2013.
D. o. E. United Nations and P. D. Social Affairs, “World population prospects 2019: Highlights (st/esa/ser.a/423),” Technical report, 2019.
D. Pimentel and M. Burgess, “Soil erosion threatens food production,” Agriculture, vol. 3, pp. 543–554, 2013.
M. D. Sadare and S. Admane, “A review on plant without soil hydroponics,” IJRET: International Journal of Research in Engineering and Technology, vol. 2, no. 3, pp. 299–304, 2013.
A. Van, H. Gieling, and A. Ruijs, “Equipment for hydroponics installations,” IJRET: International Journal of Research in Engineering and Technology, pp. 102–141, 2002.
“Leafy vegetables,” https://www.visconfreshproduce.com/leafy-vegetables/. accessed Aug. 2021.
“The best vegetables in the world grow in your kitchen,” https://lagrangette.tech/. accessed Aug. 2021.
T.-H. Wu, C.-H. Chang, Y.-W. Lin, L.-D. Van, and Y.-B. Lin, “Intelligent plant care hydroponic box using iottalk.” IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016.
C. Peuchpanngarm, P. Srinitiworawong, W. Samerjai, and T. Sunetnanta, “Diy sensor-based automatic control mobile application for hydroponics.” Fifth ICT International Student Project Conference (ICT-ISPC), 2016.
R. Rajkumar and R. Dharmaraj, “A novel approach for smart hydroponic farming using iot,” International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), vol. 5, no. 5, 2018.
S. Nandhini, S. Bhrathi, D. D. Goud, and K. P. Krishna, “Smart agriculture iot with cloud computing, fog computing and edge computing,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 9, no. 2, 2019.
S. Yi, C. Li, and Q. Li, “A survey of fog computing: Concepts, applications and issues.” Proceedings of the 2015 Workshop on Mobile Big Data (Mobidata ’15), 2015.
V. Sucharith, P. Prakash, and G. N. Iyer, “Agrifog – a fog computing based iot for smart agriculture,” International Journal of Recent Technology and Engineering (IJRTE), vol. 7, no. 6, 2019.
M. I. Alipio, A. E. M. D. Cruz, J. D. A. Doria, and R. M. S. Fruto, “A smart hydroponics farming system using exact inference in bayesian network.” IEEE 6th Global Conference on Consumer Electronics (GCCE), Nagoya, 2017, pp. 1–5.
K. P. Ferentinos, L. D. Albright, and N. R. Scott, “Modeling ph and electrical conductivity in hydroponics using artificial neural networks,” IFAC Proceedings Volumnes, vol. 33, no. 19, pp. 172–178, 2000.
M. A. Zamora-Izquierdo, J. Santa, J. A. Martinez, V. Martinez, and A. F. Skarmeta, “Smart farming iot platform based on edge and cloud computing,” Biosystems Engineering, Intelligent Systems for Environmental Applications, 2018.
A. Jukan, F. Carpio, X. Masip, A. J. Ferrer, N. Kemper, and B. U. Stetina, “Fog- to-cloud computing for farming: Low-cost technologies, data exchange, and animal welfare,” Computer, vol. 52, no. 10, pp. 41–51, 2019.
“What is mongodb,” https://www.mongodb.com/what-is-mongodb, accessed Aug. 2021.
C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation functions: Comparison of trends in practice and research for deep learning,” ArXiv, vol. abs/1811.03378, 2018.
“About node.js,” https://nodejs.org/en/about/, accessed Aug. 2021.
https://github.com/la-grangette/plantings-datasets, accessed Nov. 2021.
