University of Southern Queensland researchers are developing a new artificial intelligence smartphone app which will help cotton farmers stop a major pest from flying under the radar. Near impossible to identify and count with the naked eye, the silverleaf whitefly has increased in prevalence in recent years, considerably reducing the end value of the crop.
This is because their sticky honeydew excretions contaminate cotton lint and prevent it from being processed.University of Southern Queensland researchers are developing a new artificial intelligence smartphone app which will help cotton farmers stop a major pest from flying under the radar. Near impossible to identify and count with the naked eye, the silverleaf whitefly has increased in prevalence in recent years, considerably reducing the end value of the crop.#
To make identification easier, USQ 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 plant leaves on a weekly basis to determine if control action is required,” 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,” she said.
The first version of the app was tested by agronomists and researchers in the 2019/20 season in two cotton growing regions.
The project’s principal researcher Long said feedback from first time users of the app is helping the research team design the next version.
“Agronomists responded positively to the logging capability in the app and with further refinements being incorporated into the second version. We expect the app to be much faster than manually counting whiteflies and referencing threshold advice contained in the industry’s Pest Management Guide,” he said.
“Currently agronomists take pest measurements on a field by field basis but with the app, the potential exists to efficiently collate data at a regional scale which with would enable area wide management of silverleaf whitefly that would greatly assist current efforts with managing insecticide resistance,” he added.
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.
“Refining the app to detect a range of cotton insects will further aid agronomists and better inform pest control management decisions,” Long said.
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