An adaptive neuro-fuzzy inference system-based irrigation sprinkler system for dry season farming

Silas Soo Tyokighir, Joseph Mom, Kingsley Eghonghon Ukhurebor, Gabriel Igwue


In recent years, the management of irrigation systems has emerged as one of the most pressing concerns in the agricultural industry, especially in areas that experience dry seasons. In this research, an adaptive neuro-fuzzy inference system (ANFIS)-based irrigation system that uses a hot and cold sprinkler mechanism is presented. The goal of the system is to reduce the amount of water needed for farming and increase crop output during dry seasons. Adaptive control of water release is achieved via the use of MATLAB and the ANFIS model. This is done in response to changes in soil moisture, ambient temperature, and crop water demand. According to the findings, the suggested system performs noticeably better than conventional irrigation methods in terms of both the amount of water used and the number of crops produced.


Agricultural sustainability; Climate change; Food security; Irrigation system; MATLAB

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).