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An Insight into Fuzzy Logic Computation Technology and Its Applications in Agriculture and Meteorology


Affiliations
1 Tamil Nadu Agricultural University, Coimbatore 03., India
 

Speaking of recent advances, many computing technologies have been applied to several domains and have proved to provide more approximate and acceptable results. Fuzzy logic being one of them has been very useful in solving many real-world problems that are inherent for their uncertainty, complexity, impreciseness and a high degree of randomness. Soft computing aims to mimic human thinking and thus solve problems as a human does. The systems embedded with one or more soft computing technologies tend to make decisions quicker (reducing the processing timeframe) and more accurate in the light of uncertain and indefinite data. This paper aims at an extensive review of fuzzy logic also unraveling some of the applications of the same in the field of agricultural science and meteorology.

Keywords

Artificial Intelligence; Computation Technology; Fuzzy Logic; Virtual Reality
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  • Akperov Imran, Sakharova Luydmila. (2017). Application of fuzzy set theory in agrometeorological models for yield estimation based on statistics. Procedia Computer Science 120 (2017) 820–829 Published by Elsevier B.V.

  • Bellman RE, Zadeh L A. (1970). DecisionMaking In A Fuzzy Environment. Federal Scientific and Technical Information Springfield, Virginia 22151.

  • Chatterjee, S., Ghosh, S., De, U K. (2011). Comparison between LDA technique and fuzzy mem-bership roster method for pre-monsoon weather forecasting. Atmósfera 24(4), 385-396.

  • Das, Tapan K., Tripathy, Asis K. (2017). Fuzzy Logic as a Tool for Rainfall Prediction: A Case Study. World Wide Journal of Multidisciplinary Research and Development. 2017; 3(11): 175-179

  • Domanska, D., & Wojtylak, M. (2010). Fuzzy weather forecast in forecasting pollution concentra-tions. Geography. Corpus id: 10090502

  • Ghosh, Sudipta & Dutta, Arpan & Chowdhury, Suman & Paul, Gopal. (2014). Weather Prediction by the use of Fuzzy Logic. Journal of Mechanics of Continua and Mathematical Sciences. 8 . 1228-1241. 10.26782/ jmcms.2014.01.00006.

  • Irawan, A M., Nugroho, H A., Simanjuntak, P P., and Sugiarto., S I. (2019). Forecast of Tropical Cyclone Occurrences based on Fuzzy Logic Algorithm. IOP Conf. Series: Earth and Environmental Science 303 (2019) 01204.doi:10.1088/1755-1315/303/1/012044

  • Jantakoon, Nitaya. (2016). Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model. Mathematics and Statistics, 4(4): 95-100, 2016. DOI: 10.13189/ms.2016.040401

  • Jimoh, R. G., Olagunju, M., Folorunso, I.O., Asiribo, M.A. (2013). Modeling Rainfall Prediction using Fuzzy Logic. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 4, June 2013

  • Kurniawan, A. P., Jati, A. N., and Azmi, F. (2017). Weather prediction based on fuzzy logic algorithm for supporting general farming automation system. 5th International Conference on Instrumentation, Control, and Automation (ICA), Yogyakarta, 2017, pp. 152-157, doi: 10.1109/ICA.2017.8068431.

  • Lu,Jing., Xue, Shengjun., Zhang,Xiakun., Zhang, Shuyu., and Lu, Wanshun.(2014). Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment. Atmosphere 2014, 5(4), 788-805; https://doi.org/10.3390/atmos5040788

  • Malik, A., Kumar, A., Salih, S. Q., Kim, S., Kim, N. W., Yaseen, Z. M., & Singh, V. P. (2020). Drought index prediction using advanced fuzzy logic model: Regional case study over kumaon in india. PLoS One, 15(5) doi:http://dx.doi.org/10.1371/journal.pone.0233280

  • Marimin and Mushthofa. (2013). Fuzzy Logic Systems and Applications in Agro-industrial Engi-neeringand Technology. Proceedings of 2nd International Conference on Adaptive and Intelligent Agroindustry (ICAIA). September 16 – 17, 2013, IPB International Convention Center - Bogor – Indonesia .

  • Murtha, J. (1995). Applications of fuzzy logic in operational meteorology. Scientific Services and Professional Development Newsletter, Canadian Forces Weather Service, 42–54.

  • Pandey P, Litoriya R, Tiwari A. (2018). A framework for fuzzy modelling in agricultural diagnos-tics. Journal Europeen Des Systemes Automatises, 51(4-6), 203-223.

  • Patel, Dipi A., and Christian, R.A. (2012). Ambient Atmospheric Temperature Prediction Using Fuzzy Knowledge-Rule Base for Inland Cities in India. World Applied Sciences Journal 20 (11): 1448-1452, 2012. DOI: 10.5829/idosi.wasj.2012.20.11.1517

  • Philomine Roseline T, Ganesan N, Clarence J M Tauro. (2015). A study of applications of fuzzy logic in various domains of Agricultural sciences. International Journal of Computer Applications (0975 – 8887).

  • Sadeghi-Niaraki, A., Kisi, O., & Soo-Mi Choi. (2020). Spatial modeling of long-term air tempera-tures for sustainability: Evolutionary fuzzy approach and neuro-fuzzy methods. PeerJ, doi:http://dx.doi.org/10.7717/peerj.8882

  • Safar, Noor Zuraidin., Ramli, Azizul Azhar., Mahdin, Hirulnizam., Ndzi, David., Khalif, Muham-mad. (2019). Rain prediction using fuzzy rule based system in North-West Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1572-1581

  • Shao, J., 2000: Fuzzy Categorization of Weather Conditions for Thermal Mapping. J. Appl. Meteor., 39, 1784–1790, https://doi.org/10.1175/1520-0450-39.10.1784.

  • Silver, Micha; Svoray, Tal; Karnieli, Arnon; Fredj, Erick. (2020).Improving weather radar precipi-tation maps: A fuzzy logic approach. Atmospheric Research, Volume 234, article id.104710. doi: 10.1016/j.atmosres.2019.104710

  • Singla, Manish K., Deep Kaur, Harkamal., Nijhawan, Parag. (2019). Rain Prediction using Fuzzy Logic. International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019

  • Singla, S., Singla, P., & Rai, H. M. (2010). Agrometeorological weather forecasting system using soft computing. International Journal of Advanced Research in Computer Science, 1(3).

  • Vema, Vamsikrishna, Sudheer K P, Chaubey, I. (2019). Fuzzy inference system for site suitability evaluation of water harvesting structures in rain-fed regions. Agricultural Water Management 218 (2019) 82–93 – Elsevier.

  • Zadeh L A. (1996). Fuzzy logic – Computing with words. IEEE Transactions on Fuzzy Systems 4, NO. 2, May 1996.


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  • An Insight into Fuzzy Logic Computation Technology and Its Applications in Agriculture and Meteorology

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Authors

Sowmiyaa S.
Tamil Nadu Agricultural University, Coimbatore 03., India
Moghana Lavanya S
Tamil Nadu Agricultural University, Coimbatore 03., India
Mahendran K
Tamil Nadu Agricultural University, Coimbatore 03., India
Geethalakshmi V
Tamil Nadu Agricultural University, Coimbatore 03., India

Abstract


Speaking of recent advances, many computing technologies have been applied to several domains and have proved to provide more approximate and acceptable results. Fuzzy logic being one of them has been very useful in solving many real-world problems that are inherent for their uncertainty, complexity, impreciseness and a high degree of randomness. Soft computing aims to mimic human thinking and thus solve problems as a human does. The systems embedded with one or more soft computing technologies tend to make decisions quicker (reducing the processing timeframe) and more accurate in the light of uncertain and indefinite data. This paper aims at an extensive review of fuzzy logic also unraveling some of the applications of the same in the field of agricultural science and meteorology.

Keywords


Artificial Intelligence; Computation Technology; Fuzzy Logic; Virtual Reality

References





DOI: https://doi.org/10.13005/ojcst13.0203.06