Predictive farming
Project overview
Project title
Predictive farming: improving risk management with AI-enhanced weather forecasts and crop models
Project description
This Project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI-enhanced weather forecasts and crop models into farm management and planning tools. This is expected to result in farmers making more informed decisions that optimise resource use, boost crop yields, and enhance overall farm productivity and profitability while minimising environmental impact.
Supervisory team
University Supervisors
| Name of university supervisor | Dr Brian Collins Dr Thong Nguyen-Huy |
| Name of university | University of Southern Queensland |
| Email address | brian.collins@unisq.edu.au thong.nguyen-huy@unisq.edu.au |
| Faculty | Centre for Sustainable Agricultural Systems (CSAS) Institute for Life Sciences and the Environment (ILSE) |
CSIRO Supervisor
| Name of CSIRO supervisor | Dr Andrew Schepen |
| Email address | andrew.schepen@csiro.au |
| CSIRO Business Unit | Environment |
Industry Supervisor 1
| Name of industry supervisor | Mr Stephen Attard |
| Name of business/organisation | AgriTech Solutions Pty Ltd |
| Email address | Steve@agritechsolutions.com.au |
Industry Supervisor 2
| Name of industry supervisor | Mr Fabian Gallo |
| Name of business/organisation | Hydrotech Monitoring Pty Ltd T/A HTM Complete |
| Email address | fabian@hydrotechmonitoring.com.au |
Further details
| Primary location of student | University of Southern Queensland, 487-535 West Street , Darling Heights QLD 4350 |
| Industry engagement component location | Primary Industry Engagement component location – AgriTech Solutions, 343 Old Clare Road, McDesme QLD 4807 Secondary Industry Engagement component location – Hydrotech Monitoring Pty Ltd T/A HTM Complete, 6 Tostevin Street, Tolga QLD 4882 |
| Other locations | CSIRO Dutton Park, 41 Boggo Road, Dutton Park QLD 4102 |
| Ideal student skillset | A background in agriculture, meteorology, hydrology, or environmental science, with proficiency in statistical analysis and data processing. Strong programming skills in R (preferable) and/or Python, and experience or interest in weather prediction or climate models. Knowledge of machine learning, AI techniques, and cloud computing for data processing and model deployment Familiarity with crop models (APSIM, DSSAT, AquaCrop), irrigation systems, and agricultural practices is advantageous. Strong analytical, critical thinking, and problem-solving skills, along with experience in literature review, scientific writing, and effective presentation of research findings. |
| Apply | UniSQ |