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Irrigation is one of the most important food-water-energy problems in smart farms. It has a huge impact on the growth of food, while the water and energy cost for irrigation is the main cost of smart farms. In this paper, we propose an on-demand irrigation scheduling system which considers not only hourly numerical weather prediction to avoid over-irrigation but also TOU price model of electricity to minimize irrigation cost. To evaluate our design, we conduct extensive simulations with real-world trace data from Austin. The result shows that our proposed method can save 7.97% water and energy resources and reduce the amortized cost by 25.34% compared to a soil moisture based irrigation method.

Minimizing amortized cost of the on-demand irrigation system in smart farms