#agriculture

A&F

This project will develop economically and sustainability optimised silage feeding strategies for northern beef producers. The expected outcome is to combine feeding trials on commercial properties in the NT with economic and sustainability modelling to develop improved silage feeding strategies. This project will allow data-driven decision making by northern cattle producers to optimise feeding strategies for better economic and sustainability outcomes. 

Servers at the WA Observatory

This project develops an AI-driven system to identify individual cows, and monitor and detect their health and behaviour, through the use of facial recognition, posture analysis, and thermal imaging. The expected outcome is early detection of illness and abnormal activity of cows. This project will support productivity, reduce losses, and improve animal welfare in the dairy industry.

A&F

This project investigates ecological interactions that influence disease spread in tree crop horticultural systems. The expected outcomes are improved understanding of ecological drivers of the dynamics of diseases and ecological intervention/restoration strategies for disease management. The potential benefit is chemically limited sustainable disease management in horticulture, benefiting industry and the environment.

Environment picture

This project will investigate the use of artificial intelligence (AI) to improve weather forecasts and discover how AI forecasts can advance farming decisions by coupling with crop models and smart farming tools. This is an exciting opportunity to develop or integrate novel AI-enhanced weather forecasts into real world modelling applications, for example in the sugarcane industry. With research being undertaken alongside real farm advisors, your research can help industry to optimise resource use and enhance overall farm productivity while minimising environmental impact.

A&F

This project aims to integrate novel automated disease surveillance technology into real-time management of crop diseases for improved yield and sustainability outcomes. The expected outcome is grower recommendations for crop diseases informed by real-time automated disease surveillance. The potential benefit is improved productivity and sustainability through reduced crop losses and chemical inputs.