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. I hope you are not required to take a photo each and every time for the AI to tell you what it is? It should be an option otherwise all that’s needed is to pin a location (point or area) and select the type of weed….not too dissimilar to how we currently log a pest animal in feral scan (eg: fox, deer, pig etc etc). I imagine at a basic level, there will be core functionality to log “sightings” and also “control/removal”…similar to Feral Scan.

    If looking to develop more detailed functionality, then:
    record method used to control/remove the weed which should cater for several “visits” back to the location as it could include
    – hand slashing,
    – mechanical slashing
    – followed by herbicide on the regrowth
    – and confirmation its been completely removed

  2. Excellent news – very keen to be a beta tester if helpful

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