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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
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Principal investigator

Dr Mostafa Rahimi Azghadi

Research organisation

Project led by


Sugar Research Australia (SRA)

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

  • Location


  • Organisation type

    Research funding body, Research service providers

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