Autonomous site-specific irrigation control: the current state and a vision of a future system [Keynote]

McCarthy, Alison and Smith, Rod and Gillies, Malcolm (2014) Autonomous site-specific irrigation control: the current state and a vision of a future system [Keynote]. In: 2nd Digital Rural Futures Conference 2014, 25-27 Jun 2014, Toowoomba, Australia.

	Video (Published Version) Alison McCarthy Presentation.mht Download (159kB)
	Slideshow (Published Version) McCarthy_Smith_Gillies_DRF2014.ppt Download (12MB)

Abstract

Irrigation decision-making systems can automatically determine irrigation timing and/volume requirements. NCEA has developed control strategies and sensors to automate irrigation management, reduce labour, and improve water productivity and profit. NCEA's control frameworks 'VARIwise' and 'AutoFurrow' incorporate one or a combination of the following: infield sensing of irrigation application, soil-water status and plant growth and fruit load; hydraulic models for irrigation application; crop production models for predicting crop performance under different irrigation scenarios; optimisation procedure for processing data and determining appropriate irrigation control signals; and actuation hardware for application control.
An integrated, real-time, site-specific irrigation control system has been evaluated on a surface irrigation and centre pivot irrigation system on a cotton crop in Jondaryan, QLD in 2011/12 and 2012/13. The control system determined site-specific irrigation application with data from a weather station, soil-water sensors and camera-based crop monitoring sensing systems for vegetation and cotton fruit load. Field trials demonstrated yield improvements of 10-11% and water savings of 5-12 %.
Current field trials are identifying the data input and measurement and actuation spatial resolution requirements for the control strategies; developing and evaluating control strategies which optimise both irrigation and fertigation application to maximise yield; and investigating control strategies based on artificial intelligence. This presentation will provide an overview of the NCEA's current irrigation control research and the envisaged irrigation automation system of the future.

Statistics for this ePrint Item

Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Keynote)
Refereed:	No
Item Status:	Live Archive
Additional Information:	This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source.
Faculty/School / Institute/Centre:	Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Faculty/School / Institute/Centre:	Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 -)
Date Deposited:	22 Apr 2015 01:44
Last Modified:	17 Jul 2017 04:08
Uncontrolled Keywords:	automation; control; agriculture
Fields of Research (2008):	09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070105 Agricultural Systems Analysis and Modelling 07 Agricultural and Veterinary Sciences > 0799 Other Agricultural and Veterinary Sciences > 079901 Agricultural Hydrology (Drainage, Flooding, Irrigation, Quality, etc.)
Fields of Research (2020):	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400799 Control engineering, mechatronics and robotics not elsewhere classified 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300207 Agricultural systems analysis and modelling 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300201 Agricultural hydrology
Socio-Economic Objectives (2008):	D Environment > 96 Environment > 9609 Land and Water Management > 960905 Farmland, Arable Cropland and Permanent Cropland Water Management
URI:	http://eprints.usq.edu.au/id/eprint/26887

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