Lead Researchers: Cathie Lavis, Dale Bremer, Jack Fry and Jared Hoyle, Kansas State University;  Benjamin Wherley and Ambika Chandra, Texas A&M AgriLife Research;  Jay Ham, Tony Koski and Yaling Qian, Colorado State University
Contact: Jay Ham
Industry Partner: The Toro Company, USGA

Abstract:
Landscape irrigation strategies usually rely on calendar or evapotranspiration (ET) schedules that completely ignore soil moisture. Soil moisture sensors (SMS) may improve irrigation accuracy but are not widely used. Our proposed objectives are to: 1) Evaluate irrigation trigger thresholds by measuring turf canopy responses to soil moisture using SMS; and 2) Compare SMS-based irrigation scheduling effectiveness among several SMS types and with traditional and ET-based irrigation scheduling. The study would be comprised of three field studies, two under rainout shelters and a third in a larger, uncovered area, and would be conducted on three turfgrasses. The new, low-cost SMS with internet of things (IoT) connectivity (wireless underground sensor network, or WUSN), developed by Dr. Ham, CSU, will also be evaluated in our proposed studies. The team’s new low-cost soil sensor measures water content and soil temperature and contains a novel temperature-controlled chamber that quantifies   thermal response. The sensor was successfully calibrated against gravimetric samples on fairways at three golf courses over the summer season and will be tested in different settings in 2021. A novel IoT cellular carrier board reads the soil moisture sensors and sends the data to the cloud via a LoRa network installed with the help of industry partner AeXonis. The team invested considerable effort to make the system low power so it can operate all spring, summer, and fall on a small battery pack – no solar panel required. The entire carrier board system has been designed to fit in a small 6-inch valve box normally used in irrigation system installation.

Project Background:
Improving turf and landscape irrigation management systems using internet-of-things (IoT) sensors, machine learning, and cloud-based systems can make technologies cheaper and more user friendly. The overall goal is to conserve water by accelerating widespread use of SMS for landscape irrigation control by reducing costs and complexity in their use.