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New app to stop little cotton pests flying under the radar

Near impossible to identify and count with the naked eye, silverleaf whitefly has increased in prevalence in recent years.

To make identification easier, University of Southern Queensland researchers Dr Alison McCarthy and Dr Derek Long, in collaboration with Queensland Department of Agriculture and Fisheries researcher Dr Paul Grundy, are developing a new artificial intelligence smartphone app with funding from the Cotton Research and Development Corporation.

“Traditionally, sampling is labour-intensive and done manually, with growers and their agronomists having to closely monitor the changes in the numbers of pests across hundreds of cotton plants on a weekly basis to determine if control action is required,” Dr McCarthy said.

“We identified that machine vision could automate the pest counting on each leaf by using infield cameras and image analysis software. We have since enabled these vision detection algorithms to be used on a smartphone device,” she said.

“Through an app, agronomists can then use real-time photo capture for pest counting which offers reduced sampling times, more precise detection and recording of pests, increased sampling consistency between field personnel and improvement for the timing of control decisions.”

The first version of the app was tested by agronomists and researchers in the 2019-20 season in two cotton growing regions. An updated version of the app will be deployed for testing during the 2020-21 cotton season after which further steps are anticipated to be undertaken towards commercial release.

  • Led by
    Cotton Research and Development Corporation (CRDC)
  • In collaboration with
    Department of Agriculture and Fisheries Queensland (DAFQ)

Related organisations

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Cotton

Cotton Research and Development Corporation (CRDC)

The Cotton Research and Development Corporation (CRDC) delivers outcomes in cotton research, development and extension (RD&E) for the Australian …
  • Location

    Australia wide

  • Organisation type

    Research funding body