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Eghonghon Ukhurebor, Kingsley
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Electrical & Electronics Engineering

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An adaptive neuro-fuzzy inference system-based irrigation sprinkler system for dry season farming Tyokighir, Silas Soo; Mom, Joseph; Eghonghon Ukhurebor, Kingsley; Igwue, Gabriel
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7834

Abstract

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.
Co-Authors Igwue, Gabriel Mom, Joseph Tyokighir, Silas Soo