INTEGRATING FUZZY LOGIC INTO SMART AGRICULTURE SYSTEMS FOR BETTER YIELD PREDICTIONS

Authors

  • Sukhpreet Kaur Sidhu Department of Mathematics, Akal University, Talwandi Sabo, Punjab, India

Keywords:

Fuzzy logic, fuzzy decision-making, modern agriculture

Abstract

Agricultural systems are inherently complex, with multiple factors affecting crop yield, pest management, irrigation, soil health, and climate conditions. Traditional decision-making tools often struggle to accommodate the uncertainty and vagueness associated with agricultural data. Fuzzy set theory and fuzzy logic provide a framework for managing imprecision, allowing farmers, agronomists, and decision-makers to make more informed and flexible decisions. This paper explores the application of fuzzy set theory in various aspects of agriculture, focusing on how it aids in irrigation management, crop disease detection, pest control and overall farm management, among other areas. The paper highlights case studies and research advancements that showcase the practical benefits of adopting fuzzy logic in agriculture.

References

Bin, L., Shahzad, M., Khan, H., Bashir, M. M., Ullah, A., & Siddique, M. (2023). Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology. Sustainability, 15(18), 13874.

Elijah, O. A., Akintola, K. G., &Temitope, O. (2018). Fuzzy logic-based detection of tomato crop diseases. Journal of Agricultural Engineering and Technology, 25(3), 120-130.

Gao, L., Zhang, J., Wang, H., & Li, X. (2021). Integrating machine learning with fuzzy logic for improved agricultural decision making. Artificial Intelligence in Agriculture, 4(2), 78-88.

Henri E.Z. Tonnang, HerveBisseleua, Lisa Biber-Freudenberger, Christian Borgemeister et al. (2017), Advances in crop insect modelling methods—Towards a whole system approach, Ecological Modelling, 354(4), 88-103.

Keshtkar, A., Asadi, S., & Shams, M. (2013). Fuzzy logic-based irrigation control system for precision agriculture. Agricultural Water Management, 119, 1-9.

Mahajan, P., Singh, R., &Sahu, A. (2015). A fuzzy logic approach to yield prediction in rice crops. Agricultural Systems, 132, 63-74.

Mir, R., Bashir, A., &Hussain, I. (2020). Precision irrigation management using fuzzy logic and IoT: A case study in smart farming. Computers and Electronics in Agriculture, 175, 105489.

Nasr, A., Hanifi, M., &Mohammadi, B. (2010). Fuzzy logic-based irrigation management for water conservation in arid regions. Water Resources Management, 24(4), 783-796.

Patil, P.R., Kulkarni, U.P., & Desai, B.L. (2012), Fuzzy Logic Based Irrigation Control System Using Wireless Sensor Network For Precision Agriculture, Agricultural and Food Sciences, Engineering, Computer Science.

Sahoo, D., Jena, R., &Mohanty, S. (2017). Integrated pest management in rice using fuzzy logic. Crop Protection, 99, 110-116.

Shearer, P., & Jones, D. (2019). Fuzzy logic and qualitative data in yield prediction models. Journal of Agricultural Economics, 54(2), 145-159.

Singh, P., & Sharma, N. (2020). Application of fuzzy logic for early diagnosis of rice crop diseases. Computers in Agriculture, 47(5), 320-330.

Sran, S. S., Singh, J., & Kaur, L. (2018). Structure free aggregation in duty cycle sensor networks for delay-sensitive applications. IEEE Transactions on Green Communications and Networking, 2(4), 1140–1149.

Yin, X., Gao, S., & Liu, Y. (2013). Fuzzy logic in integrated pest management: A cotton crop case study. Crop Protection, 41, 56-65.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353

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Published

2024-12-29

How to Cite

Sukhpreet Kaur Sidhu. (2024). INTEGRATING FUZZY LOGIC INTO SMART AGRICULTURE SYSTEMS FOR BETTER YIELD PREDICTIONS. Journal Punjab Academy of Sciences, 24, 80–85. Retrieved from https://jpas.in/index.php/home/article/view/103