Abstract
Bangladesh is heavily dependent on agriculture for its crop production, food supply, and crop rotation. About 50% of the population in Bangladesh is working in the agriculture sector; agriculture occupies 70% of the country’s territory. To ensure a bountiful harvest, a soil condition suitable for cultivation and the judicious use of irrigation is essential. A fuzzy neural network-controlled irrigation controller system was developed using the research presented here. The system comprises a feedback Fuzzy Neural Network (FNN) controller that keeps track of important system measurements using sensors. The controller bases its findings on crop production, which guides it in determining when it is appropriate to irrigate. MATLAB may assign triangular and trapezoidal membership functions to every input variable. This inference engine uses Max-Min methods, which serve to derive the optimum answer for every case. Also, water consumption is lessened, and freshwater supplies are thereby protected. the system is created and tested for plant growth that reduces water usage by about 50–60% and reduces energy generating costs by the same amount. Improved irrigation management can be achieved when FNN is combined with data logging. By implementing this strategy, the overall energy use, water demand, total energy use, battery, and power control unit expenses can be reduced.
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Chowdhury, F.H., Raisa, R.A., Azad, M.S.U., Kaiser, M.S., Mahmud, M. (2022). Low-Cost Stand-Alone Smart Irrigation System: A Case Study. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_28
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DOI: https://doi.org/10.1007/978-981-16-7597-3_28
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