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