Skip to Content Skip to Navigation

Reducing herbicide usage on sugarcane farms in reef catchment areas with precise robotic weed control

The project will rely on the pioneering “deep learning” technology being developed by JCU and AutoWeed to detect and spray priority sugarcane weeds. In the first year of the project, hundreds of thousands of images of sugarcane farmers’ crops will be collected, labelled and fed into deep learning models to train the weed and crop detection system. Every time the spraying system is used it will collect more data, so the deep learning models can further improve their performance over time. The second year will focus on developing and trialling the herbicide delivery component of the project.

Project date

1 Aug 2020-1 Aug 2022
Visit website

Principal investigator

Dr Mostafa Rahimi Azghadi

Research organisation

Project led by

Sugar

Sugar Research Australia (SRA)

SRA invests in and manages a portfolio of research, development and adoption projects that drive productivity, profitability and sustainability for …

Sugar
  • Location

    Australia

  • Organisation type

    Research funding body, Research service providers

Logo for Sugar Research Australia (SRA)

Collaborators

Industries

Related research projects

Search all research projects
Logo for Mackay Whitsunday Cane to Creek
Sugar

Mackay Whitsunday Cane to Creek

This project is accelerating the adoption of improved nutrient and pesticide management strategies that contribute to the reduction of dissolved …
  • Led by

    Sugar Research Australia (SRA)

  • Start date

    1 Sep 2020

  • Research organisation

    Sugar Research Australia (SRA)

Have questions?

Find out how we can help you.

Find answers to our most frequently asked questions on research projects, commercial opportunities, organisations and more. 

Still have questions or have feedback on the site? Please get in touch by completing our enquiry form.