Jay Ham, Yaling Qian, Tony Koski, Dale Bremer, and Cathie Lavis 

ABSTRACT

Landscape irrigation, predominantly turfgrass, can account for almost 60% of urban water use in the western U.S. However, improved irrigation scheduling could save 25-30% of this water without affecting esthetics. We believe turfgrass irrigation will be transformed by a revolution in the autonomous control of sprinkler systems – driven by a new generation of sensors, wireless connectivity, big data, and A.I. However, field research on the best way to use IoT sensors, machine learning, and other breakthrough technologies to achieve this goal is lacking.  The objective of this project is to conduct field studies to improve irrigation management using internet-of-things (IoT) sensor technology coupled with machine learning and cloud computing.  Research in 2020 included: 1) development of low-cost soil sensors for measuring soil moisture and temperature, 2) engineering an IoT carrier board that can use cellular, LoRa, or WiFi to send soil data to the cloud, 3) field testing of these technologies at three golf courses and other properties in Fort Collins, and 4) additional plot studies of soil-sensor-triggered irrigations at Kansas State University. Our new soil sensor performed well compared to research-grade sensors – the CSU sensor is low-cost (<$5), making it economical to deploy in large numbers across the landscape.  Our IoT wireless carrier board can be buried below ground and is essentially “invisible” on the landscape while still delivering soil moisture data to the cloud hourly.  EM38 surveys were done on the golf courses to develop management zones and optimize the placement of our sensors. Our industry partner, AeXonis, installed wireless LoRa gateways at all three golf courses and the IIC field site. This puts our team in excellent position to start testing our below ground wireless soil networks (patent pending) in 2021, and continue our work on optimizing irrigation of turfgrass using the latest technology and A.I.