DESIGN AND DEVELOPMENT OF A SOLAR-POWERED PRECISION IRRIGATION SYSTEM USING INDIGENIZED SOIL MOISTURE SENSORS AND RAINWATER HARVESTING

Abstract This study introduces an innovative, integrated approach to enhancing water-use efficiency in semi-arid agricultural regions through the design and implementation of a solar-powered precision irrigation system. The system synergistically combines low-cost, indigenously developed soil moisture sensors, rainwater harvesting infrastructure, and sustainable solar energy to offer an autonomous and scalable irrigation solution. The soil moisture sensors, engineered for affordability and reliability, demonstrated strong agreement with the oven-drying reference method (R² = 0.89), enabling accurate, real-time monitoring and automated irrigation control. Powered by a 55W solar panel, the system stores harvested rainwater in a 65-liter tank optimized for a 303.95 m² roof catchment area. A hybrid irrigation configuration, integrating both drip and sprinkler systems, was employed to maximize water distribution efficiency while minimizing losses due to evaporation and runoff. Field evaluations revealed a 3–4-fold reduction in water consumption compared to conventional flood irrigation, highlighting the system’s effectiveness in resource conservation. Designed with adaptability and cost-effectiveness in mind, this solution is particularly well-suited for smallholder farmers in water-stressed environments. The research underscores the critical role of integrating renewable energy and precision irrigation technologies to advance sustainable agriculture, improve crop productivity, and strengthen resilience in the face of increasing water scarcity.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-23
DOI
https://doi.org/10.5281/zenodo.15259647
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
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article

DESIGN AND DEVELOPMENT OF A SOLAR-POWERED PRECISION IRRIGATION SYSTEM USING INDIGENIZED SOIL MOISTURE SENSORS AND RAINWATER HARVESTING

Zenodo (CERN European Organization for Nuclear Research)
Soil Moisture and Remote Sensing
article

DESIGN AND DEVELOPMENT OF A SOLAR-POWERED PRECISION IRRIGATION SYSTEM USING INDIGENIZED SOIL MOISTURE SENSORS AND RAINWATER HARVESTING

article en

Abstract

Abstract This study introduces an innovative, integrated approach to enhancing water-use efficiency in semi-arid agricultural regions through the design and implementation of a solar-powered precision irrigation system. The system synergistically combines low-cost, indigenously developed soil moisture sensors, rainwater harvesting infrastructure, and sustainable solar energy to offer an autonomous and scalable irrigation solution. The soil moisture sensors, engineered for affordability and reliability, demonstrated strong agreement with the oven-drying reference method (R² = 0.89), enabling accurate, real-time monitoring and automated irrigation control. Powered by a 55W solar panel, the system stores harvested rainwater in a 65-liter tank optimized for a 303.95 m² roof catchment area. A hybrid irrigation configuration, integrating both drip and sprinkler systems, was employed to maximize water distribution efficiency while minimizing losses due to evaporation and runoff. Field evaluations revealed a 3–4-fold reduction in water consumption compared to conventional flood irrigation, highlighting the system’s effectiveness in resource conservation. Designed with adaptability and cost-effectiveness in mind, this solution is particularly well-suited for smallholder farmers in water-stressed environments. The research underscores the critical role of integrating renewable energy and precision irrigation technologies to advance sustainable agriculture, improve crop productivity, and strengthen resilience in the face of increasing water scarcity.

Zenodo (CERN European Organization for Nuclear Research)
University of Minnesota (US), University of Agriculture Faisalabad (PK)
Zero hunger
Openalex Percentile: Top 55%
Soil Moisture and Remote Sensing
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