Technical validation of the Predictive Optimal Water and Energy Irrigation (POWEIr) controller for solar-powered drip irrigation

• Validates a precision irrigation controller for resource-constrained farmers. • Controller optimizes water and energy to produce case-specific irrigation schedule. • Demonstrated up to 44 Jordan and Morocco. • Observed scheduling uncertainty was examined to identify algorithm modifications. • Illustrates feasibility of precision irrigation controller with minimal sensors. To feed the growing population, agriculture production must be intensified using existing resources. Sustainable agriculture intensification is particularly important in low and middle income countries (LMICs), which disproportionately experience food insecurity. This study evaluates the performance of a precision irrigation controller for solar-powered drip irrigation (SPDI) under real-world operating conditions. SPDI has the potential to increase water use efficiency and reduce fossil fuel use for irrigation. Precision irrigation technology could lower SPDI operating costs and enable sustainable irrigation practices among farmers with varied expertise. Despite these benefits, the adoption of SPDI and precision irrigation is limited in LMICs due to high investment costs and system complexity. Previous work proposed the Predictive Optimal Water and Energy Irrigation (POWEIr) controller as a precision irrigation solution that could meet the needs of farmers in resource-constrained contexts. This study quantifies the POWEIr controller performance in terms of water and energy savings, irrigation reliability, and system cost. The controller reduced water and energy use compared to typical farmer practice by up to 44% and 43%, respectively, while maintaining crop yield over three growing seasons. The controller used solar power for irrigation, but relied on a buffer battery to execute irrigation schedules. A yield loss sensitivity analysis found that increasing the controller’s use of solar energy by about 40% would have been sufficient to reliably irrigate with solar alone. These results suggest that the POWEIr controller could enable reliable, low-cost SPDI systems, and if adopted, could make sustainable irrigation practices more accessible to farmers in LMICs.

Authors

Institutions

Publication Details

Journal
Smart Agricultural Technology
Published
2026-04-22
DOI
https://doi.org/10.1016/j.atech.2026.102147
Primary Topic
Irrigation Practices and Water Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Technical validation of the Predictive Optimal Water and Energy Irrigation (POWEIr) controller for solar-powered drip irrigation

Samer Talozi, Ahmed Wifaya, Fiona Grant, Vinay Nangia et al.
Smart Agricultural Technology
Irrigation Practices and Water Management
article

Technical validation of the Predictive Optimal Water and Energy Irrigation (POWEIr) controller for solar-powered drip irrigation

Samer Talozi, Ahmed Wifaya, Fiona Grant, Vinay Nangia, Carolyn Sheline, Anas Mansouri, Susan Amrose, Ammar Namarneh, Shane Pratt, Amos G. Winter, Georgia van De Zande
article en

Abstract

• Validates a precision irrigation controller for resource-constrained farmers. • Controller optimizes water and energy to produce case-specific irrigation schedule. • Demonstrated up to 44 Jordan and Morocco. • Observed scheduling uncertainty was examined to identify algorithm modifications. • Illustrates feasibility of precision irrigation controller with minimal sensors. To feed the growing population, agriculture production must be intensified using existing resources. Sustainable agriculture intensification is particularly important in low and middle income countries (LMICs), which disproportionately experience food insecurity. This study evaluates the performance of a precision irrigation controller for solar-powered drip irrigation (SPDI) under real-world operating conditions. SPDI has the potential to increase water use efficiency and reduce fossil fuel use for irrigation. Precision irrigation technology could lower SPDI operating costs and enable sustainable irrigation practices among farmers with varied expertise. Despite these benefits, the adoption of SPDI and precision irrigation is limited in LMICs due to high investment costs and system complexity. Previous work proposed the Predictive Optimal Water and Energy Irrigation (POWEIr) controller as a precision irrigation solution that could meet the needs of farmers in resource-constrained contexts. This study quantifies the POWEIr controller performance in terms of water and energy savings, irrigation reliability, and system cost. The controller reduced water and energy use compared to typical farmer practice by up to 44% and 43%, respectively, while maintaining crop yield over three growing seasons. The controller used solar power for irrigation, but relied on a buffer battery to execute irrigation schedules. A yield loss sensitivity analysis found that increasing the controller’s use of solar energy by about 40% would have been sufficient to reliably irrigate with solar alone. These results suggest that the POWEIr controller could enable reliable, low-cost SPDI systems, and if adopted, could make sustainable irrigation practices more accessible to farmers in LMICs.

Smart Agricultural TechnologyVol. 14
Jordan University of Science and Technology (JO), National Agricultural Research Institute (PG), Water Authority of Jordan (JO), GE Global Research (United States) (US), Massachusetts Institute of Technology (US)
United States Agency for International Development
Openalex Percentile: Top 15%
Irrigation Practices and Water Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.