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Identifying sensors for better IPM in Cotton

Silverleaf whitefly (SLW), mites and aphids gathering under cotton leaves are not easily detected manually because of diurnal movement or patchy distribution. Left untreated, they can reduce the value of a cotton crop through feeding damage, or by depositing honeydew that reduces quality. Effective management relies on their accurate and timely detection and quantification to identify the need for treatment. This project has successfully developed a proof-of-concept machine-vision sensing approach able to discriminate pest infestations with a beta app now being tested in the field. The algorithm includes machine learning based on thousands of SLW nymph image samples. The sensor has the potential to further improve management of these key pests.

Project date

1 Jul 2018-31 Dec 2021
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Principal investigator

Alison McCarthy

Project led by

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 …

Cotton
  • Location

    Australia wide

  • Organisation type

    Research funding body

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Characteristics of disease suppressive cotton farming systems and soils understood

Soil borne diseases continue to be one of the major constraints to cotton production. This project helps to identify soil …
  • Led by

    Cotton Research and Development Corporation (CRDC)

  • Start date

    1 Sep 2019

  • Research organisations

    Commonwealth Scientific and Industrial Research Organisation (CSIRO), Cotton Research and Development Corporation (CRDC), New South Wales Department of Primary Industries (NSW DPI), Queensland Department of Agriculture and Fisheries (QDAF), The University of Queensland (UQ), University of Southern Queensland (USQ)

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