Lawns make up the largest irrigated crop by surface area in North America, and carries with it a demand for over 9 billion gallons of freshwater each day. Despite recent developments in irrigation control and sprinkler technology, state-of-the-art irrigation systems do nothing to compensate for areas of turf with heterogeneous water needs. In this work, we overcome the physical limitations of the traditional irrigation system with the development of a sprinkler node that can sense the local soil moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally- computed schedule. A model is then developed to compute moisture movement from runoff, absorption, and diffusion. Integrated with an optimization framework, optimal valve scheduling can be found for each node in the space. In a turf area covering over 10,000 square feet, two separate deployments spanning a total of 7 weeks show that MAGIC can reduce water consumption by 23.4% over traditional campus scheduling, and by 12.3% over state-of-the- art evapotranspiration systems, while substantially improving conditions for plant health. In addition to environmental, social, and health benefits, MAGIC is shown to return its investment in 16-18 months based on water consumption alone.
['Daniel A. Winkler', 'Robert Wang', 'Francois Blanchette', 'Miguel Á. Carreira-Perpiñán', 'Alberto E. Cerpa']
MAGIC: model-based actuation for ground irrigation control