Fast and efficient identification is critical for reducing the likelihood of weed establishment and for appropriately managing established weeds. Traditional identification tools require either knowledge of technical morphological terminology or time-consuming image matching by the user. In recent years, deep learning computer vision models have become mature enough to enable automatic identification. The major remaining bottlenecks are the availability of a sufficient number of high-quality, reliably identified training images and the user-friendly, mobile operationalization of the technology. Here, we present the first weed identification and reporting app and website for all of Australia. It includes an image classification model covering more than 400 species of weeds and some Australian native relatives, with a focus on emerging biosecurity threats and spreading weeds that can still be eradicated or contained. It links the user to additional information provided by state and territory governments, flags species that are locally reportable or notifiable, and allows the creation of observation records in a central database. State and local weed officers can create notification profiles to be alerted of relevant weed observations in their area. We discuss the background of the WeedScan project, the approach taken in design and software development, the photo library used for training the WeedScan image classifier, the model itself and its accuracy, and technical challenges and how these were overcome.
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14 November 2024
WeedScan, a weed reporting system for Australia using an image classification model for identification
Alexander N. Schmidt-Lebuhn,
Matt Bell,
Carsten Eckelmann,
Dane Evans,
Andreas Glanznig,
Rongxin Li,
Andrew Mitchell,
Tomas Mitchell-Storey,
Michael Newton,
Liam O'Duibhir,
Richard Southerton,
Emily Thomas,
Hanwen Wu
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computer vision
identification
surveillance
weed management
