Real-time control approaches for site-specific irrigation and fertigation optimisation

McCarthy, Alison and Nguyen, Tai and Raine, Steven (2014) Real-time control approaches for site-specific irrigation and fertigation optimisation. In: 2nd Digital Rural Futures Conference 2014, 25-27 Jun 2014, Toowoomba, Australia.

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Abstract

Automated irrigation and fertigation site-specific control systems offer labour and water savings, and crop productivity improvements for growers, where spatial variability of water requirements exists within a field. Real-time irrigation control strategies for surface and pressurised irrigation systems have been developed that adapt to infield soil and plant measurements collected in real-time. 'Sensor-based' control strategies directly use measurements to make irrigation decisions; and 'model-based' control strategies use a model (often calibrated with sensor input) to aid irrigation decisions. Model-based control strategies can aim for specific end of season characteristics. However, model-based control strategies often use off-the-shelf, black box industry models that may not be updated with the development of the new varieties, and may not consider all the soil-plant-water relations.
A hybrid Artificial Neural Network (ANN) and Bayesian model is being used for training and predicting crop dynamics based on historical and real-time infield data. A game theory and artificial intelligence-based approach will provide an inbuilt self-learning capability for new crop conditions to achieve site-specific irrigation optimisation and real-time adaptive control. This paper will present an overview and comparison the adaptive control approaches for irrigation and fertigation, and considerations for their use under commercial conditions on surface and pressurised irrigation systems.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Speech)
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 / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 22 Apr 2015 01:54
Last Modified: 17 Jul 2017 04:03
Uncontrolled Keywords: advanced process control; artificial intelligence; agriculture
Fields of Research : 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.)
Socio-Economic Objective: D Environment > 96 Environment > 9609 Land and Water Management > 960905 Farmland, Arable Cropland and Permanent Cropland Water Management
B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820301 Cotton
URI: http://eprints.usq.edu.au/id/eprint/26886

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