This research aims to improve the efficiency of variable rate irrigation (VRI) systems using data collected from sensors on unmanned aircraft. The research uses images from multispectral and thermal sensors on UAS to model evapotranspiration spatially across fields. This evapotranspiration model will be used to prescribe VRI applications to optimize water use. The research presents benefits like reduced pumping costs, nitrate leaching, and yield losses from over/under irrigation. Activities include collecting sensor data on UAS flights, processing images, modeling evapotranspiration, and developing VRI prescriptions. Outcomes include conference presentations and modeling evapotranspiration using UAS sensor data.

1. Improving Variable Rate Irrigation Efficiency Using a
Real-time Soil Moisture Adaptive Control Model
Informed by Sensors Deployed on Unmanned Aircraft.
Mitch Maguire
Department of Biological Systems Engineering
Advisors: Wayne Woldt and Christopher Neale

2. Research Topic
• Use data collected from unmanned aerial system (UAS) to improve
variable rate irrigation (VRI) efficiency.
• Potential benefits of properly managed VRI system
• Reduced energy cost and nitrate leaching due to reduced pumping
• Reduced yield loss due to over/under irrigation
• How does this work coincide with the DWFI’s mission?
• Maximize our yield to water use ratio
• Increase productivity of irrigated agriculture

3. Research Topic
• What is Variable Rate Irrigation?
• VRI systems can spatially vary water applied to a field
• How to determine amount of water to apply, and
when to apply?
• That’s what this research looks to answer
• Using data collected by UAS on regular basis (weekly)
• Model evapotranspiration spatially over a field
• Apply model outputs to produce VRI prescriptions
VRI zone control

4. Research Activities
Integrating UAS and sensors
• Unmanned Aircraft Systems: Tempest and FireFly6 Pro
• Sensors: MicaSense RedEdge multispectral and Flir Tau 2 thermal infrared sensors

5. Research Activities
Collecting and Processing Data
• Collect several hundred images per flight
• Process images into a single “stitched” image using Pix4D
Typical flight plan with collected images
Images aligned and stitched into a single image

6. Research Activities
Collecting and Processing Data
• Images are processed into orthomosaics
NIR band reflectance Red band reflectance Surface temperatureNDVI

7. Research Activities
Using Processed Data for Modeling Evapotranspiration
• Spatial EvapoTranspiration Modeling Interface (SETMI)
• Model Energy Balance Flux Components

8. Research Activities
Using Processed Data for Modeling Evapotranspiration
Irrigation Prescription
Using energy balance and soil water balance
models (SETMI), we can model evapotranspiration
to be used in prescribing irrigation events.

9. Research Outcomes
• Poster presentations
• 2017 Water For Food Conference
• Maguire, M. S., Woldt, W. E., Neale, C. M. U. (2017) A Production Field Scale Review of Agricultural Image
Processing and Dual Sensor Integration for Unmanned Aircraft Systems. 2017 Robert B. Daugherty Water for
Food Global Conference. Lincoln, NE.
• UNL Graduate Research Fair
• Presentations at ASABE Annual International Meeting (2017 and 2018)
• Maguire, M. S., Barker, J.B., Neale, C. M. U., Woldt, W. E., Suyker, A. (2018). Modeling Evapotranspiration using
Multispectral and Thermal Infrared Imagery Acquired with a Low Altitude Unmanned Aerial System. ASABE
Paper No. 1801334. St. Joseph, MI: ASABE.
• Maguire, M. S., Woldt, W. E., Neale, C. M. U., Frew, E. W., & Meyer. G. E. (2017). A Survey of Agricultural Image
Processing for Unmanned Aircraft Systems. ASABE Paper No. 1701454. St. Joseph, MI: ASABE.
• Maguire, M. S., Woldt, W. E., Frew, E. W., Smith, J., & Elston, J. (2017). Thermal Infrared and Multi-spectral Dual
Sensor Integration for Unmanned Aircraft Systems. ASABE Paper No. 1701455. St. Joseph, MI: ASABE.
• UAS Extension Event (April 2018) – Workshop on collecting and processing UAS data into usable products

10. Thank You
waterforfood.nebraska.edu