We’ve used artificial intelligence (AI) to identify a pest stink bug. Now we’re working with our partners on WeedScan, a new app for identifying and reporting weeds.
A photo of a hand holding a phone that is using the WeedScan app. Behind the phone is a photo of weeds.
Blackberries are delicious but one of Australia’s worst weeds.

Weeds cost Australia’s agriculture industry at least $5 billion a year. They make up about 12 per cent of our flora, a higher proportion than any other continent. Identifying and reporting harmful weeds is important for managing both farmed and natural landscapes. Enter the idea for a national scale weed identification and reporting app. 

Identifying weeds

CSIRO botanist Alexander Schmidt Lebuhn specialises in daisies. There are more than 25,000 species in this family. Many daisies, such as dandelions, are successful weeds because their seeds can travel long distances on the wind. 

In recent years, Alexander has expanded his work as a taxonomist classifying daisies. He now uses artificial intelligence (AI) to build apps to identify invasive insects and weeds. The latest app is WeedScan, a collaboration with NSW Department of Primary Industries (DPI) and the Centre for Invasive Species Solutions, funded by National Landcare. 

Alexander is coordinating field work from Tasmania to the Top End to take photos of weeds growing in bushland and on farms.

“We are working on 300 priority weed species that are causing problems in different places all over Australia,” Alexander said. 

“Firstly, we identify each weed plant carefully. Then we take photos of the weed from multiple different angles. We try to find weeds growing at different life stages, such as before it flowers and after it sets seed.

“The next step is using AI to train a weed image classification model so the AI can accurately recognise each weed species. Then the model will be embedded in an app for people to use on their phones,” he said. 

A photo of a man standing outside holding onto a large weed.
Weed experts Richie Southerton (pictured) and Andrew Mitchell are photographing weeds in the wild. The images help train an AI model to recognise priority weeds. 

Test driving WeedScan

The current weed image classification model for WeedScan contains 57 weed species. NSW DPI has built a website prototype featuring a subset of these. 

A group of farmers, land managers and biosecurity staff tested the prototype in Bathurst today. WeedScan is planned to be released for free public use in 2023. 

Once released, WeedScan app users will be able to identify priority weeds by simply taking a photo on their phone. App users will also be able to submit records to biosecurity staff, supporting local action to manage weeds. 

A close up photograph of a small weed in the ground.
Xanthium spinosum is a weed in the daisy family.


  1. it will be great to have an Australian focused id, I just hope the photos used are all as clear as the Xanthium spinosum pictured. Many identifier apps and books have low resolution images with poor isolation of the planet from the background – as well as an image that shows the plant in context. There is rarely a practical scale object either.

  2. I presume it will have a reliability scale when it makes the identification? I am thinking of the genus Picris, which has introduced and native species, all look similar, and some are threatened with extinction. Would hate to see it eradicated by accident, even if well-intended.

  3. Every part of the dandelion is edible. Weed or not weed?

  4. This would be great! I use PictureThis app and search the NSW WeedWise app, combining these two feature together with Aust specific references would be great!

  5. Looks like a good resource, but to make it better,
    look-alike native plants could be included to add certainty to the diagnosis.

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