Michigan State University (MSU) researchers have developed and are testing a low-cost irrigation monitoring system called LOCOMOS.

With LOCOMOS, the in-field sensors measure soil moisture, leaf wetness and other environmental conditions. The data is then analyzed by software that generates precise irrigation recommendations and delivers them to growers via an easy-to-use smartphone app.

The development of the system and app was facilitated through a partnership with the MSU Innovation Center. The work is led by Younsuk Dong, an assistant professor and irrigation specialist in the Department of Biosystems and Agricultural Engineering.

“The commercial-grade monitoring systems are expensive and thus not accessible to most farmers,” Dong said. “The data they generate can also be difficult to interpret. A simpler system that uses low-cost sensors was needed, and LOCOMOS can be used with a smartphone, which almost all farmers can easily access today.”

In 2021, Dong and his team – which includes Martin Chilvers, a professor and field crops pathologist in the Department of Plant, Soil and Microbial Sciences – received a three-year, US$426,000 grant from the U.S. Department of Agriculture’s Natural Resources Conservation Service (NRCS).

LOCOMOS has been tested in a variety of cropping systems, including multiple field crops, blueberries and potatoes, while others are forthcoming.

For the NRCS project, researchers are partnering with five farms across Michigan to evaluate LOCOMOS in corn, soybean and tomato fields. The study is ongoing, but based on the first three years of on-farm demonstration data, sensor-based irrigation scheduling has improved water use efficiency at each of the five farms.

In the corn and soybean fields, Dong said LOCOMOS has enhanced irrigation water use efficiency while not increasing disease incidence. In the tomato field, sensor-based scheduling saved 30 percent on water use versus the grower’s typical irrigation method.

On top of the water savings, the team also found that LOCOMOS has potential in other areas, such as informing timings for fungicides or other sprays.

Dong said the next major step in the research and development process is automation, in particular for irrigation. Once data is collected, an algorithm would create the optimal irrigation strategy and trigger the system to implement it.