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Research Thesis Topic

Self-learning Technology for Irrigation and Fertiliser Management

Topic ID

Thesis Topic/Title
Self-learning Technology for Irrigation and Fertiliser Management


Water savings in the Australia's irrigated agricultural industries can be achieved through irrigation control systems that automatically and optimally adapt to the crop water requirements from real-time sensor data. A common approach for irrigation is from crop stress and soil-water status measurements. Model-based control strategies enable irrigation and crop optimisation using a calibrated crop production model to predict which irrigation application and timing maximises productivity. This project will investigate control strategies and/or crop production models for optimisation of irrigation application. The model used may be industry developed black-box models; alternatively, deep learning may be used for training and predicting crop dynamics based on infield data.

Principal Supervisor

Associate Supervisors

Research Affiliations
  • Institute for Agriculture and the Environment
  • National Centre for Engineering in Agriculture
  • School of Agricultural, Computational and Environmental Sciences
  • School of Mechanical and Electrical Engineering

Field of Research
  • Agriculture, Land and Farm Management
  • Crop and Pasture Production
  • Electrical and Electronic Engineering

Application Open Date

Application Close Date

USQ Scholarship Applications

Other Scholarship Funding Details

Pre-approved for Ethics

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic

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