Autonomous sitespecific irrigation control engineering a future irrigation

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

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

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

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

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

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

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

Surface irrigation hydraulic optimisation n Real-time optimisation of surface irrigation using ‘Auto. Furrow’ n 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

Real-time adaptive irrigation control n Control methodology developed that can adapt to different irrigation systems and crops Actuation Sensors Control strategy

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

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

Simulation of irrigation management

Simulation of fodder production B Treatment Irrigate all field (A) Irrigate only non-waste areas (B) Irrigate according to EM 38 variability (C) C Water use (ML/ha) 4. 24 ± 0. 00 Biomass yield (kg/ha) 8486. 2 ± 242. 5 3. 76 ± 0. 00 8486. 2 ± 242. 5 3. 04 ± 0. 26 8540. 3 ± 41. 7

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

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

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

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

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

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

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

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

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

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

Surface irrigation trial

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

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

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

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

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

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

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

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