Controlling weeds, precisely.
The latest in precision weed control research from the @Sydney_Uni Weeds Research Team in Narrabri and Camden. Narrabri, New South WalesJoined July 2018
This machine’s algorithm is capable of distinguishing between #BroadleafWeeds, #GrassWeeds, and #ginger with a great level of accuracy and is helping to detect and spray for #weeds in a ginger crop. To read more about this project, visit bit.ly/3o0cmJK
@GeezaColeman & @WeedAI_news are seeking growers to join a beta testing group for feedback user experience.
📷 All you need are the images to upload.
DM @GeezaColeman if you're interested in participating …
Weed-AI now hosts:
🗄 17 datasets, 20,891 images
🌾 8 crops + fallow
🌱 28 species + poaceae/broadleaf groups
🌏 datasets from: 🇦🇺🇩🇰🇦🇷🇺🇲
If you've got datasets of weeds or need data for a project, check out Weed-AI
weed-ai.sydney.edu.au
weed-ai.sydney.edu.au is an open data platform that the Sydney Informatics Hub is building to enable R&D into computer vision methods to identify and control weeds. It's great to see new data coming into the system!
🚨 New publication 🚨
Michael Walsh & @SBPowles look at the role of harvest weed seed control in Australian grain production systems.
What does it mean for weed recognition?
Read the full paper here or summary below from @WeedSmartAU@SiaSydneypublish.csiro.au/CP/pdf/CP21647
🚨 New publication 🚨
Michael Walsh & @SBPowles look at the role of harvest weed seed control in Australian grain production systems.
What does it mean for weed recognition?
Read the full paper here or summary below from @WeedSmartAU@SiaSydneypublish.csiro.au/CP/pdf/CP21647
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