1. Autonomous site-specific irrigation control: engineering a future irrigation management system Dr Alison McCarthy, Professor Rod Smith and Dr Malcolm Gillies National Centre for Engineering in Agriculture Institute for Agriculture and the Environment mccarthy@usq.edu.au

2. NCEA’s irrigation research Water storage and distribution Infield application Monitoring tools Technology support

3. Cotton irrigation in Australia Cotton industry accounts for >20% of irrigation water used in Australia Site-specific irrigation automation presents opportunities for improved water use efficiencies

4. Need for automation in surface irrigation Surface irrigation is common in Australia Furrow – cotton, grains, sugar Bay/Border – pasture Labour cost and labour shortage Siphons started manually Cut-off time determined manually

5. Surface irrigation automation hardware Automation is often time based and inflexible Currently lacks ability to adapt to field conditions Rubicon automation hardware and software: (already in commercial use in Dairy Industry)

6. Variable-rate technology for LMIMs User-defined prescription maps Four out of 100 growers in Georgia with variable-rate Farmscan systems are still used Poor irrigation prescription support Farmscan

7. Irrigation automation research Automation enables high resolution data capture and analysis and control  Hydraulic optimisation  Real-time adaptive irrigation control  On-the-go plant and soil sensing technology  Internet-enabled sensing and control integrated into the irrigation system

8. Surface irrigation hydraulic optimisation Real-time optimisation of surface irrigation using ‘AutoFurrow’ Real-time optimisation typically involves: 1.Inflow measurement 2.Time for advance front to about midway down the field 3.Real-time estimation cut-off time that will give maximum performance for that irrigation

9. Real-time adaptive irrigation control SensorsControl strategyActuation SensorsControl strategy Control methodology developed that can adapt to different irrigation systems and crops

10. VARIwise control framework Use sensed data to determine irrigation application/timing ‘VARIwise’ simulates and develops irrigation control strategies at spatial resolution to 1m 2 and any temporal resolution Control strategies based on difference between measured and desired performance

11. Surface irrigation system Overhead irrigation system Irrigation control system - strategies 1. Sensors 2. Control strategy 3. Real-time irrigation adjustment

12. Simulation of irrigation management

13. Simulation of fodder production TreatmentWater use (ML/ha)Biomass yield (kg/ha) Irrigate all field (A)4.24 ± 0.008486.2 ± 242.5 Irrigate only non-waste areas (B) 3.76 ± 0.008486.2 ± 242.5 Irrigate according to EM38 variability (C) 3.04 ± 0.268540.3 ± 41.7 BC

14. Iterative Learning Control (ILC): Uses the error between the measured and desired soil moisture deficit after the previous irrigation,... to adjust the irrigation volume of the next irrigation event. ‘Learns’ from history of prior error signals to make better adjustments. Iterative Hill Climbing Control (IHCC): Tests different irrigation volumes in ‘test cells’ to determine which volume produced desired response Model predictive control (MPC) A calibrated crop model simulates and predicts the next required irrigation, i.e. volumes and timings  according to evolving crop/soil/weather input  separately for all cells/zones  can choose alternative end-of-season predicted targets Adaptive control strategies

15. How much infield data is needed? Iterative Learning Control (ILC) – best where data is sparse Model Predictive Control (MPC) – needs intensive data set to maximise yields

16. Surface irrigation system Overhead irrigation system Irrigation control system - sensors 1. Sensors 2. Control strategy 3. Real-time irrigation adjustment

17. Plant sensing platforms Ground-based platform for surface irrigation Vehicle-based platform for surface irrigation Overhead-mounted platform for centre pivots/lateral moves

18. Soil-water variability sensing Estimated by correlating electrical conductivity and infield soil-water sensors

19. Advance rate sensing using cameras Image from 8m high tower: Image from 20m tower:

20. Surface irrigation system Overhead irrigation system Irrigation control system - actuation 1. Sensors2. Control strategy 3. Real-time irrigation adjustment

21. Adaptive control of surface irrigation Accurate hydraulic models are available to determine irrigation application distributions Link hydraulic model to a crop production and soil model and control strategy:  Crop model estimates crop response to different irrigation applications  Control strategy determines irrigation applications  Hydraulic model determines spatial distribution of irrigation

22. Surface irrigation adaptive control trial Controlled flow rate to achieve irrigation depths along furrow

23. Advance rate monitoring Real-time optimisation of flow rate from advance rate Before adjustment:After adjustment:

24. Surface irrigation trial

25. Surface irrigation system Overhead irrigation system Irrigation control system - actuation 1. Sensors2. Control strategy 3. Real-time irrigation adjustment

26. Adaptive control of centre pivot irrigation Three replicates of MPC, ILC and FAO-56 with different targets and data inputs (weather, soil, plant) One span with flow meters, valves

27. Weather, soil and plant measurements Variability in soil types High rainfall season 617mm rain Infield weather station: On-the-go plant sensor: Electrical conductivity map

28. Irrigation adjustment Irrigation application controlled on one span Lower irrigation flow rate:Higher irrigation flow rate:

29. Adaptive control of centre pivot Plant data input led to higher yield than only soil and weather data input

30. Autonomous irrigation management Autonomous irrigation management is achievable Field trials Using plant sensing and adaptive control strategies for surface and centre pivot irrigation systems With reduced labour and water applied, improved yield Further research on data types and resolutions required for adaptive control Further work proposed for commercial scale trials

31. Vision – precision irrigation framework Integrated irrigation decision-making tool for the cotton industry Demonstrate, evaluate in other crops and regions Optimise both irrigation and fertiliser application - in cotton industry up to 30% nitrogen lost

32. Acknowledgements Cotton Research and Development Corporation for funding support Lindsay Evans, Nigel Hopson, Neil Nass and Ian Speed for providing field trial sites Dr Malcolm Gillies for programming support Dr Jochen Eberhard for data collection assistance