The Performance of Water Irrigation Control using Fuzzy-GA Approach
Abstract
Irrigation in agriculture uses around 70% of freshwater resources globally, but traditional systems often result in ineffective utilization through rigid schedules or skewed decision-making. This article proposes an improved fuzzy logic controller developed using a Genetic Algorithm (GA) to optimize soil moisture control. The GA optimizes the fuzzy membership functions within 50 generations to enhance irrigation efficiency. Simulation and experimental results show that the fuzzy-GA controller maintained soil moisture at values close to the desired value of 25.1% with lower error rates, saving 858 mL more water than manual irrigation and 16 mL more than conventional fuzzy control. The results confirm the potential of fuzzy-GA systems in optimizing irrigation efficiency and ensuring sustainable use of water in agriculture. The fuzzy-genetic algorithm (Fuzzy-GA) improves fuzzy logic control by maintaining soil moisture at a target level of 25.1%, with a very low steady-state error of 0.03783%.
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Allen, L.N., & MacAdam, J.W. (2020). Irrigation and water management. In K.J. Moore, M. Collins, C.J. Nelson, & D.D. Redfearn (Eds.), Forages (pp. 497–513). Wiley. https://doi.org/10.1002/9781119436669.ch27
Allen, R.G., Pereira, L.S., & Smith, M. (1998). Crop Evapotranspiration (guidelines for computing crop water requirements) (Vol. 56). FAO - Food and Agriculture Organization of the United Nations. https://www.fao.org/4/x0490e/x0490e00.htm
Bajpai, P., & Kumar, M. (2010). Genetic algorithm-an approach to solve global optimization problems. Indian Journal of Computer Science and Engineering, 1, 199–206.
Bwambale, E., Abagale, F.K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324. https://doi.org/10.1016/j.agwat.2021.107324
Davies, F.S., & Albrigo, L.G. (1983). Water relations of small fruits. Additional Woody Crop Plants, 89–136. https://doi.org/10.1016/B978-0-12-424157-2.50009-4
Ganivet, E. (2020). Growth in human population and consumption both need to be addressed to reach an ecologically sustainable future. Environment, Development and Sustainability, 22(6), 4979–4998). https://doi.org/10.1007/s10668-019-00446-w
Razali, N.M., & Geraghty, J. (2011, July 6–8). Genetic algorithm performance with different selection strategies in solving TSP. In Proceedings of the World Congress on Engineering 2011, 2, 1134–1139. https://www.iaeng.org/publication/WCE2011/WCE2011_pp1134-1139.pdf
Islam, M.S., Tumpa, S., Afrin, S., Ahsan, M.N., Haider, M.Z., & Das, D.K. (2021). From over to optimal irrigation in paddy production: What determines over-irrigation in Bangladesh? Sustainable Water Resources Management, 7(3), 35. https://doi.org/10.1007/s40899-021-00512-0
Jaiswal, S., & Ballal, M.S. (2020). Fuzzy inference based irrigation controller for agricultural demand side management. Computers and Electronics in Agriculture, 175, 105537. https://doi.org/10.1016/j.compag.2020.105537
Kadyampakeni, D.M., Morgan, K.T., Zekri, M., Ferrarezi, R., Schumann, A., & Obreza, T.A. (2017). Citrus irrigation management. EDIS, 2017(5). https://doi.org/10.32473/edis-ss660-2017
Krishnan, R.S., Julie, E.G., Robinson, Y.H., Raja, S., Kumar, R., Thong, P.H., & Son, L.H. (2020). Fuzzy logic based smart irrigation system using Internet of Things. Journal of Cleaner Production, 252. https://doi.org/10.1016/j.jclepro.2019.119902
Li, S.X., Wang, Z.H., Malhi, S.S., Li, S.Q., Gao, Y.J., & Tian, X.H. (2009). Chapter 7 Nutrient and water management effects on crop production, and nutrient and water use efficiency in dryland areas of China. Advances in Agronomy, 102, 223–265. https://doi.org/10.1016/S0065-2113(09)01007-4
Liang, C., & Shah, T. (2023). IoT in agriculture: The future of precision monitoring and data-driven farming. Eigenpub Review of Science and Technology, 7(1). https://studies.eigenpub.com/index.php/erst
Liang, H., Zou, J., Zuo, K., & Khan, M.J. (2020). An improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system. Mechanical Systems and Signal Processing, 142, 106708. https://doi.org/10.1016/j.ymssp.2020.106708
Marganingrum, D., & Santoso, H. (2019). Evapotranspiration of Indonesia tropical area. Jurnal Presipitasi : Media Komunikasi Dan Pengembangan Teknik Lingkungan, 16(3), 106–116. https://doi.org/10.14710/presipitasi.v16i3.106-116
MathWorks. (2024). Fuzzy Logic Toolbox: User’s Guide (r2024b). The MathWorks, Inc.
Niu, X., Feng, G., Jia, S., & Zhang, Y. (2021). Control of brushless DC motor based on fuzzy rules optimized by genetic algorithm used in hybrid vehicle. Journal of Computational Methods in Sciences and Engineering, 21(4), 951–968. https://doi.org/10.3233/JCM-204628
Perez-Blanco, C.D., Hrast-Essenfelder, A., & Perry, C. (2020). Irrigation technology and water conservation: A review of the theory and evidence. Review of Environmental Economics and Policy, 14(2), 216–239. https://doi.org/10.1093/REEP/REAA004
Saha, H.N., Roy, R., Chakraborty, M., & Sarkar, C. (2021). Development of IoT‐based smart security and monitoring devices for agriculture. Agricultural Informatics, 147–169. https://doi.org/10.1002/9781119769231.ch8
Sutikno, T., Subrata, A.C., & Elkhateb, A. (2021). Evaluation of fuzzy membership function effects for maximum power point tracking technique of photovoltaic system. IEEE Access, 9, 109157–109165. https://doi.org/10.1109/ACCESS.2021.3102050
Tebbal, I., & Hamida, A.F. (2023). Effects of crossover operators on genetic algorithms for the extraction of solar cell parameters from noisy data. Engineering, Technology and Applied Science Research, 13(3), 10630–10637. https://doi.org/10.48084/etasr.5417
Violino, S., Figorilli, S., Ferrigno, M., Manganiello, V., Pallottino, F., Costa, C., & Menesatti, P. (2023). A data-driven bibliometric review on precision irrigation. Smart Agricultural Technology, 5, 100320. https://doi.org/10.1016/j.atech.2023.100320
Xie, J., Chen, Y., Gao, P., Sun, D., Xue, X., Yin, D., Han, Y., & Wang, W. (2022). Smart fuzzy irrigation system for litchi orchards. Computers and Electronics in Agriculture, 201, 107287. https://doi.org/10.1016/j.compag.2022.107287
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