Experimental and Practical Study of a Smart Irrigation System Utilizing the Internet of Things
Water scarcity is a global issue that presents significant challenges to sustainable development. Recent Food and Agriculture Organization (FAO) evaluations revealed a concerning trend: more than half of agricultural water is wasted. The widespread use of manual irrigation systems, combined with diminishing water resources, necessitates efficient and effective irrigation methods. This study aims to address this issue by introducing a novel irrigation control system based on an Arduino mechanism. The goal of this research is to design and test an efficient and sustainable irrigation system that maximizes water usage, minimizes waste, and reduces electricity consumption. The study used an Arduino-controlled irrigation system equipped with soil moisture sensors. These sensors were strategically placed to monitor soil moisture levels and trigger irrigation when necessary. The system’s performance was evaluated using a series of experimental trials on small and medium-sized farm plots. The system required approximately 20% less water than traditional manual irrigation methods. Furthermore, there was a significant reduction in electricity consumption for water pumps, with an average 20% savings per year. The ease of integration into existing farm setups and the reduction in manual labor are also significant benefits. This system assists to conserve vital water resources and promotes sustainable agricultural development by optimizing water use and reducing electricity consumption.
Authors
- Sahar Daghagh Yazd (ORCID: https://orcid.org/0000-0001-7541-1910)
- Javad Farrokhi Derakhshandeh (ORCID: https://orcid.org/0000-0002-6812-9148)
- Sara Attaran (ORCID: https://orcid.org/0009-0001-1258-9268)
Institutions
- American University of the Middle East (KW)
- Ferdowsi University of Mashhad (IR)
Publication Details
- Journal
- Journal of Irrigation and Drainage Engineering
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1061/jidedh.ireng-10403
- Citations
- 4
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 2.40