These research projects all involve at least one industry partner and one IIC university partner and have brought a minimum of 1:1 matching support from non-federal sources.

Optimizing Irrigation of Turfgrass Using Soil Sensors, IoT LoRa Technology, and Artificial Intelligence
Jay Ham, Yaling Qian, Tony Koski, Dale Bremer, and Cathie Lavis

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…

Next Generation Technology for Monitoring Edge-of-Field (EoF) Water Quality in Agriculture
Emmanuel Deleon, Erik Wardle, Jay Ham, Dylan Casey, and Christina Welch

Irrigated agriculture has the potential to be a contributor to non-point source water quality issues. One of the most effective methods to quantify sediment and nutrient losses from fields is edge of field water quality monitoring. Industry standard sampling equipment can be cost prohibitive, thus limiting its use and reducing the availability of reliable data…

Using stochastic simulations to connect models of crop evapotranspiration to observations from wireless sensors
Matthew R. Lurtz and Ryan R. Morrison

We describe the implementation of a cost-effective ecosystem-monitoring network and subsequently use the observations to inform well-known evapotranspiration equations to test if a model can replicate processes that generate the data. In this study, we designed a monitoring station that measures within-canopy temperature, soil-moisture presence at varying depths, and green-leaf temperature using a contactless infrared thermometer…

Evaluating WISE Performance for Maize Corn when Updated with Remote Sensing-based Actual Crop Coefficients: Full and Deficit Irrigation
Edson Costa-Filho, José L. Chávez, Allan A. Andales, and Ansley J. Brown

Several decision-making tools for irrigation scheduling, such as the Water Irrigation Scheduler for Efficient application (WISE), rely on the soil-water deficit method, with tabulated crop coefficients (Kc) to estimate crop evapotranspiration (ETc). However, agricultural fields often experience varying ETc rates due to surface and management conditions that depart from ideal. Remote sensing (RS) techniques could improve irrigation scheduling by accounting for near real-time on-site conditions…

Measuring crop water use with a novel Internet-of-Things sap-flow system
Maria C. Capurro, Allan Andales, Jay Ham

Monitoring crop water requirements can improve irrigation efficiency and sustainability of irrigated agriculture. Sap flow gauges can aid in its quantification and assessment. Our objective was to develop an innovative Internet-of-Things (IoT) Sap-Flow System to measure plant transpiration in real time and incorporate an advanced decision making and diagnostic tool for irrigation management. In this study, a new type of sensor, logger and algorithms were developed to measure the water flow through plant stems just like one might measure water flow through a pipe…

Field Testing of ASABE X627, Alternate Protocol #2 For Evaluating Weather Based Irrigation Controllers
Mark A. Crookston and Brent Q. Mecham

From July 17th to October 15th of 2019, field testing of irrigation controllers was undertaken to verify procedures and calculations in support of finalizing alternate protocol #2 of the proposed ASABE X627 testing standard for weather-based irrigation controllers. Three different weather stations in northeastern Colorado were utilized for reference. Ten different make/model of controllers were included, with replicates of each, for a total of 24 individual controllers. All of the controllers included in the field testing had previously been awarded the Water Sense certification label by the Environmental Protection Agency. ASABE X627 has since been finalized as ANSI/ASABE S627 DEC2020, Weather-Based Landscape Irrigation Control Systems standard…

Testing Landscape Irrigation Flow Sensors
Brent Q. Mecham

Flow sensors for landscape irrigation have long been used in conjunction with a separate flow controller, display, or compatible irrigation controller to monitor flow in residential and commercial landscape irrigation systems. Active flow monitoring conserves water, protects plant materials, and prevents site damage by detecting 1) Unscheduled flow events, 2) low flow events, and 3) high flow events caused by irrigation system component failures. Logic in the controller can then take corrective action to stop the water flow, sound an alarm or send alert messages.

AJ Brown leads a Virtual Field Day in the summer of 2020.