A Fuzzy Machine Learning Based Approach for Promoting Sustainability in Agriculture.

Authors

  • Smaranika Mohapatra Department of Information Technology, Manipal University Jaipur, Jaipur, India,
  • Neha Chaudhary Department of Computer Science Engineering, Manipal University Jaipur, Jaipur, India

Keywords:

Agriculture, sustainability, recommendation system, expert system, Mamdani Fuzzy Inference System

Abstract

Agriculture being the backbone of India, various factors affect the sustainability of the food products and he farmers. Factors like soil, geographic region, and climatic parameters impact significantly on crop yield and productivity. The farmers in India follow the traditional approach for harvesting. This is due to the lack of awareness and not having adequate knowledge of the challenges the parameters have on crop products due to which it often leads to financial and social loss to the farmers. An approach is much required to provide an efficient solution to extend help and sustainability of the farmers. The current study recommends a fuzzy logic-based recommendation system for the cash crops to help the farmers have financial and social sustainability. The proposed recommendation system is designed using a Fuzzy Inference System (FIS) using the rule-based logic approach to achieve an accuracy of 93% which is examined and validated.

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References

http://www.fao.org/india/fao-in-india/india-at-a-glance/en/

https://un-csam.org/Activities%20Files/A0902/in-p.pdf

https://www.vedantu.com/geography/introduction-to-agriculture.

B. Garg et al., Crop yield forecasting using fuzzy logic and regression model, Computers and Electrical Engineering (2017).

S Sotnik, Z Deineko, V Lyashenko, Key Directions for Development of Modern Expert Systems, (2022).

N Katkamwar, B Kumar, K Kalaskar, An expert system approach for improvement of agriculture decision. Singaporean Journal of Scientific Research, Vol5, (1), (2013).

Banerjee, G., Sarkar, U., Ghosh, I. (2021). A Fuzzy Logic-Based Crop Recommendation System. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_6.

JA Rojas, HE Espitia, LA. Bejarano, Design and Optimization of a Fuzzy Logic System for Academic Performance Prediction. Symmetry. 13(1):133, 2021.

Bělohlávek, Radim, Joseph W. Dauben, and George J. Klir, 'Applications of Fuzzy Logic', Fuzzy Logic and Mathematics: A Historical Perspective (New York, 2017; online edn, Oxford Academic, 22 June 2017), https://doi.org/10.1093/oso/9780190200015.003.0006, accessed 23 Sept. 2023.

Muhamadi, S. and Boz, I., 2022. Factors influencing farmers' perception of sustainable agriculture: a case study of Musanze District, Rwanda. International Journal of Sustainable Agricultural Management and Informatics, 8(4), pp.408-424.

https://www.tourism.rajasthan.gov.in/.

https://www.nccs.nasa.gov/services/climate-data-services.

Ahmadi, M.H.E., Royaee, S.J., Tayyebi, S. and Boozarjomehry, R.B., 2020. A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling. Engineering Applications of Artificial Intelligence, 90, p.103485.

Kumar N, Ahuja S, Kumar V, Kumar A. Fuzzy time series forecasting of wheat production. Int J Comput Sci Eng, 2:635–40, 2010.

Tina Samavat, Mostafa Nazari, Mohsen Ghalehnoie, Morteza Azimi Nasab, Mohammad Zand, Padmanaban Sanjeevikumar, Baseem Khan, "A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System", International Journal of Energy Research, vol. 2023, Article ID 7676113, 14 pages, 2023, https://doi.org/10.1155/2023/7676113.

Ben Yahia, Nesrine & Bellamine Ben Saoud, Narjès & Ben Ghezala, Henda. (2012). Integrating fuzzy case-based reasoning and particle swarm optimization to support decision making. International Journal of Computer Science Issues. 9.

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Published

23.02.2024

How to Cite

Mohapatra, S. ., & Chaudhary, N. . (2024). A Fuzzy Machine Learning Based Approach for Promoting Sustainability in Agriculture. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 730–736. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/5030

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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