Lawns make up the largest irrigated crop by surface area in North America and carry with it a demand for over 7B 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 sprinkler node in the space. In a turf area covering over 10,000ft 2 , two separate deployments with four weeks of fine-grained data collection show that DICTUM 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, DICTUM is shown to return its investment in 16 to 18 months based on water consumption alone.

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 10,000ft2, 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.

Lawns, both public and private, make up the largest irrigated crop in North America by surface area. Although there have been improvements in sprinkler head technology and weather assimilation, state-of-the-art irrigation systems do nothing to adjust for heterogeneous terrain or varying lawn environments. In this work, a computationally lightweight soil moisture movement model is developed, which allows the computation of optimal irrigation valve scheduling using standard optimization techniques. A prototype sprinkler head is produced with the ability to sense local soil moisture conditions, wirelessly communicate, and independently actuate based on the optimal schedule centrally computed. This prototype is then deployed to control two parallel irrigation systems covering a total of more than 10,000 ft$^2$ for a duration of 5 weeks. It is shown that lawn health can be maintained by using the topography of the space to take advantage of runoff to provide improved coverage while using an average of 23.4\% less water. We also show that the initial capital and operating costs of our system could be amortized by our water savings in $~$13 months while maintaining and/or improving quality of irrigation and lawn health.