Design of an Affordable, Precise Irrigation Controller that Lowers the Barrier to Water- and Energy-Sustainable Agriculture

Abstract

With climate change and population growth exacerbating global food insecurity, it has become urgent to establish more water- and energy-efficient means to raise agricultural production. Available techniques to bolster crop productivity, such as solar-powered drip irrigation (SPDI) and precision irrigation, are currently cost-prohibitive for farmers in low-and middle-income countries (LMICs), where food insecurity will be most severe. This thesis demonstrates one method to reduce the barrier to these systems, by pairing them with a Predictive Optimal Water and Energy Irrigation (POWEIr) controller that optimizes irrigation schedules to make efficient use of solar and water resources for maximum crop yield. In doing so, POWEIr also decreases SPDI system costs.

First, this work confirms the hypothesis that scheduling irrigation activity to match the availability of variable solar power enables SPDI cost savings. For a fixed irrigation system, a SPDI full-season operation simulation study was conducted and the impact of adjusting the pumping load dynamically to match solar power availability was assessed. When evaluated against conventional operation, this process of profile matching enabled a power system lifetime cost decrease of >18% while delivering 100% of the required irrigation for a simulated two-hectare Kenyan tomato farm with over 50 m well depth.

To exploit these cost and reliability benefits, this work proposes the POWEIr controller. The POWEIr controller leverages machine learning and utilizes a small set of inexpensive sensors to optimize irrigation schedules based on solar energy and crop water demand pre-dictions. The performance of the POWEIr controller was evaluated with an experimental SPDI prototype and compared to simulated typical farming practices. For the same irrigation delivered, a six-fold decrease in the required battery capacity was observed. With no batteries, the POWEIr controller still satisfied a greater fraction of the irrigation demand. Overall, compared to typical practice, the controller provided more reliable irrigation using solar power, with minimal battery usage.

High reliability at low cost necessitates that the POWEIr controller’s irrigation schedules are robust to errors in agronomy inputs and weather data. Sensitivity to these errors was assessed by evaluating the impact on simulated irrigation amounts and crop yield. It was found possible to rely on weather data from an economical station, costing $190, 83% less than a better-equipped research-quality alternative, with negligible consequences to crop yields. This conclusion held steadfast across diverse crop and soil types. The crop coefficient was the most significant factor affecting irrigation performance, thereby pointing to the need for calibration of this factor alone. This underscores the POWEIr controller’s capability to accurately optimize irrigation schedules for only essential water use while relying on affordable sensors and minimal calibration.

Finally, the POWEIr controller was piloted on farms in Jordan and Morocco and performance was benchmarked against measured local, conventional drip irrigation practices on similar farms. It provided up to 44% and 43% savings in water use and pumping energy consumption, respectively, for similar crop yields. This result demonstrates theory to practice of accessible precision agriculture technology and offers tangible evidence of the POWEIr controller’s potential to raise agricultural sustainability.