Design and Research of an Automatic Irrigation System

Traditional irrigation methods waste 30–50% of water through evaporation and runoff, exacerbating resource scarcity in drought-prone regions. This paper presents the design, simulation, and validation of a Solar-Powered Automatic Irrigation System (SPAIS) integrating IoT-based soil moisture sensing, adaptive control algorithms (Arduino), and renewable energy. Monte Carlo simulations (10,000 trials) evaluated three operational scenarios: Test 1 (6.73 L/min flow) achieved 403.66 L/day water delivery with 23.78% solar efficiency; Test 2 (20 L/min, high-demand) delivered 2,345 L/day at 11.2% efficiency, suitable for water-intensive crops like maize; Test 3 confirmed reliability under 5% sensor noise. Economic analysis revealed a 0.92-year payback for high-demand crops versus petrol alternatives, with CO2 reductions of 111.57 kg/year. The system demonstrates viability for smallholder farms in water-scarce regions like Northern Nigeria, enhancing precision agriculture through theoretical modeling and resource optimization.

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Publication Details

Published
2025-08-26
DOI
https://doi.org/10.38124/ijisrt/25aug740
Primary Topic
Wireless Sensor Networks and IoT
Type
article
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0.00
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article

Design and Research of an Automatic Irrigation System

Asagba Oghenefejiro Benedict
Wireless Sensor Networks and IoT
article

Design and Research of an Automatic Irrigation System

Asagba Oghenefejiro Benedict
article en

Abstract

Traditional irrigation methods waste 30–50% of water through evaporation and runoff, exacerbating resource scarcity in drought-prone regions. This paper presents the design, simulation, and validation of a Solar-Powered Automatic Irrigation System (SPAIS) integrating IoT-based soil moisture sensing, adaptive control algorithms (Arduino), and renewable energy. Monte Carlo simulations (10,000 trials) evaluated three operational scenarios: Test 1 (6.73 L/min flow) achieved 403.66 L/day water delivery with 23.78% solar efficiency; Test 2 (20 L/min, high-demand) delivered 2,345 L/day at 11.2% efficiency, suitable for water-intensive crops like maize; Test 3 confirmed reliability under 5% sensor noise. Economic analysis revealed a 0.92-year payback for high-demand crops versus petrol alternatives, with CO2 reductions of 111.57 kg/year. The system demonstrates viability for smallholder farms in water-scarce regions like Northern Nigeria, enhancing precision agriculture through theoretical modeling and resource optimization.

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Wireless Sensor Networks and IoT
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