autonomous site-specific irrigation control: engineering a future irrigation management system dr...
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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
NCEA’s irrigation research
Water storage and distribution Infield application Monitoring tools Technology support
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
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
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)
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
Irrigation automation research
Automation enables high resolution data capture and analysis and controlHydraulic optimisationReal-time adaptive irrigation controlOn-the-go plant and soil sensing technology
Internet-enabled sensing and control integrated into the irrigation system
Surface irrigation hydraulic optimisation
Real-time optimisation of surface irrigation using ‘AutoFurrow’
Real-time optimisation typically involves:1. Inflow measurement2. 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
Real-time adaptive irrigation control
Sensors Control strategyActuationActuation Sensors Control strategy
Control methodology developed that can adapt to different irrigation systems and crops
VARIwise control framework
Use sensed data to determine irrigation application/timing
‘VARIwise’ simulates and develops irrigation control strategies at spatial resolution to 1m2 and any temporal resolution
Control strategies based on difference between measured and desired performance
Surface irrigation system Overhead irrigation system
Irrigation control system - strategies
1. Sensors 2. Control strategy3. Real-time
irrigation adjustment
Simulation of irrigation management
Simulation of fodder production
Treatment Water use (ML/ha) Biomass yield (kg/ha)Irrigate all field (A) 4.24 ± 0.00 8486.2 ± 242.5
Irrigate only non-waste areas (B)
3.76 ± 0.00 8486.2 ± 242.5
Irrigate according to EM38 variability (C)
3.04 ± 0.26 8540.3 ± 41.7
B C
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
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
Surface irrigation system Overhead irrigation system
Irrigation control system - sensors
1. Sensors 2. Control strategy3. Real-time
irrigation adjustment
Plant sensing platformsGround-based platform for surface irrigation
Vehicle-based platform for surface irrigation
Overhead-mounted platform for centre pivots/lateral moves
Soil-water variability sensing
Estimated by correlating electrical conductivity and infield soil-water sensors
Advance rate sensing using cameras
Image from 8m high tower: Image from 20m tower:
Surface irrigation system Overhead irrigation system
Irrigation control system - actuation
1. Sensors 2. Control strategy3. Real-time
irrigation adjustment
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
Surface irrigation adaptive control trial
Controlled flow rate to achieve irrigation depths along furrow
Advance rate monitoring
Real-time optimisation of flow rate from advance rate
Before adjustment: After adjustment:
Surface irrigation trial
Surface irrigation system Overhead irrigation system
Irrigation control system - actuation
1. Sensors 2. Control strategy3. Real-time
irrigation adjustment
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
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
Irrigation adjustment
Irrigation application controlled on one span
Lower irrigation flow rate: Higher irrigation flow rate:
Adaptive control of centre pivot
Plant data input led to higher yield than only soil and weather data input
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
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
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