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A Study on Various Applications of IoT Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
The Internet of Things (IoT) advancement has transformed each piece of everyday presence by making everything more canny. Among the immense extent of IoT applications, IoT based canny agribusiness has enraptured various investigators and has used Machine Learning (ML) besides, IOT headways to lead creative explores. IoT based data driven farm the board strategies can help increase agricultural yields by orchestrating input costs, lessening disasters, and using resources even more capably. The IoT makes huge aggregate data with different characteristics taking into account region and time. To move along productivity of agribusiness through sharp estate the board, the data separating ought to be by and large around analyzed furthermore, dealt with. Tip top execution adding limit up ML opens up new entryways for data raised science as how much data assembled fabricates; ML computations could be applied to extra redesign application information and helpfulness. In this article we review existing systems have been made to the splendid agribusiness and developing considering IoT and ML freely. Furthermore, we propose clever thoughts that how should ML-IoT can be blended in such applications.
Keywords
Internet of Things (IoT), Machine Learning (ML), Artificial intelligence in IoT (MLIoT).
References
[1] M. Popa, O. Prostean,and A.S. Popa, (2019) Machine Learning Approach for Agricultural IoT In Proc. International Journal of Recent Technology and Engineering (IJRTE),(pp 22-29).
[2] Medela, et al. (2013 )IoT Multiplatform networking to monitor and control wineries and vineyards. In: Future Network and Mobile Summit,” IEEE,( pp. 1–10).
[3] Mohammad Saeid Mahdavinejad, et al (2018) Machine learning for internet of things data analysis: a survey in proc. Digital Communications and Network, (pp, 161– 175).
[4] Andreas Kamilaris, et al.(2016) Agri-IoT: A Semantic Framework for Internet of Things-enabled Smart Farming Applications,” European Union.
[5] Prem Prakash Jayaraman, et al., (2018) Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt,in proc Sensors, (pp 16, 1884).
[6] Castelli, Mauro, et al.,(2018) Supervised Learning: Classification, Reference Module in Life Sciences,in proc. Elsevier.
[7] R. L. F. Cunha, B. Silva and M. A. S. Netto,(2018) A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast," 2018 IEEE 14th International Conference on e-Science (e-Science), Amsterdam, 2018, (pp. 423-430).
[8] S. Dimitriadis and C. Goumopoulos, (2008)"Applying Machine Learning to Extract New Knowledge in Precision Agriculture Applications," 2008 Panhellenic Conference on Informatics, Samos, 2008, (pp. 100-104).
[9] T. Siddique, D. Barua, Z. Ferdous and A. Chakrabarty, "Automated farming prediction,(2017 ) Intelligent Systems Conference (IntelliSys), London, 2017, (pp. 757- 763).
[10] M. T. Shakoor, K. Rahman, S. N. Rayta and A. Chakrabarty, 2017 Agricultural production output prediction using Supervised Machine Learning techniques, 1st International Conference on Next Generation Computing Applications (NextComp), Mauritius, ( pp. 182-187).
[11] M. V. Ramesh et al. (2017), "Water quality monitoring and waste management using IoT," 2017 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, (pp. 1-7).
[12] C. J. G. Aliac and E. Maravillas,(2018) "IOT Hydroponics Management System," 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines, (pp. 1-5).
[13] R. N. Rao and B. Sridhar,(2018) "IoT based smart cropfield monitoring and automation irrigation system," 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018, pp. 478-483.
[14] A. A. Araby et al., "Smart IoT Monitoring System for Agriculture with Predictive Analysis," 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 2019,( pp. 1-4).
[15] S. Dimitriadis and C. Goumopoulos, (2008 )Applying Machine Learning to Extract New Knowledge in Precision Agriculture Applications," 2008 Panhellenic Conference on Informatics, Samos, ( pp. 100-104)
[16] O. Pandithurai, S. Aishwarya, B. Aparna and K. Kavitha,(2017) "Agro-tech: A digital model for monitoring soil and crops using internet of things (IOT)," In proc. 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), Chennai, (pp. 342-346).
[17] N. Ananthi, J. Divya, M. Divya and V. Janani,(2017) IoT based smart soil monitoring system for agricultural production," 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, ( pp. 209-214
How to cite this paper
@article{1703372,
author = {Dr. Praveen Kumar Reddy, Dr. Rajanna G S},
title = {A Study on Various Applications of IoT Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {8},
pages = {334-337},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703372.pdf},
abstract = {The Internet of Things (IoT) advancement has transformed each piece of everyday presence by making everything more canny. Among the immense extent of IoT applications, IoT based canny agribusiness has enraptured various investigators and has used Machine Learning (ML) besides, IOT headways to lead creative explores. IoT based data driven farm the board strategies can help increase agricultural yields by orchestrating input costs, lessening disasters, and using resources even more capably. The IoT makes huge aggregate data with different characteristics taking into account region and time. To move along productivity of agribusiness through sharp estate the board, the data separating ought to be by and large around analyzed furthermore, dealt with. Tip top execution adding limit up ML opens up new entryways for data raised science as how much data assembled fabricates; ML computations could be applied to extra redesign application information and helpfulness. In this article we review existing systems have been made to the splendid agribusiness and developing considering IoT and ML freely. Furthermore, we propose clever thoughts that how should ML-IoT can be blended in such applications.},
keywords = {Internet of Things (IoT), Machine Learning (ML), Artificial intelligence in IoT (MLIoT).},
month = {February},
}